• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.1
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• pp.107-119
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• 2014

The purpose of this study is to examine the current status of vascular plants and naturalized plants growing in the Seongeup Folk Village in Jeju and to consider and compare their distribution patterns and the characteristics of emergence of naturalized plants in other folk villages and all parts of Jeju, thereby exploring measures to well manage naturalized plants. The result of this study is as follows.11) The total number of vascular plants growing in Seongeup Folk Village is identified to be 354 taxa which include 93 families, 260 genus, 298 species, 44 varieties and 12 breeds. Among them, the number of naturalized plants is 55 taxa in total including 22 families, 46 genus, 53 species, and 2 varieties, which accounts for 21.7% of the total of 254 taxa identified all over the region of Jeju. The rate of naturalization in Seongeup Folk Village is 15.5%, which is far higher than the rates of plant naturalization in Hahoi Village in Andong, Yangdong Village in Gyeongju, Hangae Village in Seongju, Wanggok Village in Goseong, and Oeam Village in Asan. Among the naturalized plants identified within the targeted villages, the number of those growing in Jeju is 9 taxa including Silene gallica, Modiola caroliniana, Oenothera laciniata, Oenothera stricta, Apium leptophyllum, Gnaphalium purpureum, Gnaphalium calviceps, Paspalum dilatatum and Sisyrinchium angustifolium. It is suggested that appropriate management measures that consider the characteristics of the gateway to import and the birthplace of the naturalized plants are necessary. In the meantime, 3 more taxa that have not been included in the reference list of Jeju have been identified for the first time in Seongeup Folk Village, which include Bromus sterilis, Cannabis sativa and Veronica hederaefolia. The number of naturalized plants identified within the gardens of unit-based cultural properties is 20 taxa, among which the rate of prevalence of Cerastium glomeratum is the highest at 62.5%. On the other hand, the communities of plants that require landscape management are Brassica napus and other naturalized plants, including Cosmos bipinnatus, Trifolium repens, Medicago lupulina, Oenothera stricta, O. laciniata, Lotus corniculatus, Lolium perenne, Silene gallica, Hypochaeris radicata, Plantago virginica, Bromus catharticus and Cerastium glomeratum. As a short-term measure to manage naturalized plants growing in Seongeup Folk Village, it is important to identify the current status of Cosmos bipinnatus and Brassica napus that have been planted for landscape agriculture, and explore how to use flowers during the blooming season. It is suggested that Ambrosia artemisiifolia and Hypochaeris radicata, designated as invasive alien plants by the Ministry of Health and Welfare, should be eradicated initially, followed by regular monitoring in case of further invasion, spread or expansion. As for Hypochaeris radicata, in particular, some physical prevention measures need to be explored, such as for example, identifying the habitat density and eradication of the plant. In addition, it is urgent to remove plants, such as Sonchus oleraceus, Houttuynia cordata, Crassocephalum crepidioides, Erigeron annuus and Lamium purpureum with high index of greenness visually, growing wild at around high Jeongyi town walls. At the same time, as the distribution and dominance value of the naturalized plants growing in deserted or empty houses are high, it is necessary to find measures to preserve and manage them and to use the houses as lodging places.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.3
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• pp.41-66
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• 2017

The purpose of this research is to empirically trace the junctures of Yigye-gugok managed by Gwan-am Hong Gyeong-mo, a grandson of Yigye Hong Yang-ho who originally designed Yigye-gugok, while reviewing the features of the forms and patterns of gugok. The results of the research are as follows. 1. Ui-dong was part of the domain of the capital during the Chosun dynasty, which also is located in the city of Seoul as a matter of administrative zone. Likewisely, Yigye-gugok is taken as a special meaning for it was one and only gugok. Starting with Mangyeong Waterfall as the $1^{st}$ gok, Yigye follows through the $2^{nd}$ gok of Jeokchwibyeong Rock, the $3^{rd}$ gok of Chanunbong Peak, the $4^{th}$ gok of Jinuigang Rock, the $5^{th}$ gok of Okkyeongdae Rock, the $6^{th}$ gok of Wolyeongdam Pond, the $7^{th}$ gok of Tagyeongam Rock, the $8^{th}$ gok of Myeongoktan Stream, and the $9^{th}$ gok of Jaeganjeong Pavilion. Of these, Mangyeong Waterfall, Chanunbong Peak, and Okkyeongdae Rock are distinct for their locations in as much as their features, while estimated locations for Jinuigang Rock, Wolyeongdam Pond, Myeongoktan Stream, and Jaeganjeong Pavilion were discovered. However, Jeokchwibyeong Rock and Tagyeongam Rock demonstrated multiple locations in close resemblance to documentary literatures within secretive proximity, whereas geography, scenery, and sighted objects were considered to evaluate the 1st estimated location. Through these endeavored, it was possible to identify the shipping routes and structures for the total distance of 2.1km running from the $1^{st}$ gok to the $9^{th}$ gok, which nears Gwanam's description of 5ri(里), or approximately 1.96km for gugok. 2. Set towards the end of the $18^{th}$ century, Yigye-gugok originated from a series of work shaping the space of Hong Yang-ho's tomb into a space for the family. Comparing Yigye-gugok to other gugoks, numerous differences are apparent from beyond the rather more general format such as adjoining the $8^{th}$ gok while paving through the lower directions from the upper directions of the water. This gives rises to the interpretation such that Yigye-gugok was positioned to separate the doman of the family from those of the other families in power, thereby taking over Ui-dong. Yet, the aspect of the possession of the space lends itself to the determination that the location positioned at the $8^{th}$ gok above Mangyeongpok Waterfall representing Wooyi-dong was a consequence of the centrifugal space creation efforts. 3. While writings and poetic works were manufactured in such large quantities in Yigye-gugok whose products of setters and managers seemed intended towards gugok-do and letters carved on the rocks among others, there is yet a tremendous lack of visual media in the same respect. 'Yigye-gugok Daejacheop' Specimens of Handwriting offers the traces of Gwanam's attempts to engrave gakja at the food of Yigye-gugok. This research was able to ascertain that 'Yigye-gugok Daejacheop' Specimens of Handwriting was a product of Hong Yang-ho's collections maintained under the auspices of the National Central Museum, which are renowned for Song Shi-yeol's penmanship.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Sasang Constitutional Medicine
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• v.9 no.1
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• pp.127-153
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• 1997

Sasang-Medicine can classify all sympton with more simple classifying system than past Oriental-Medicine, because Sasang Byeon-Zeung(=classifying system of the sympton) separate by four clearly. The merit of this Sasang Byeon-Zeung can be seen more clearly on the part of the pathology of the expiratory-scattering and inspiratory-gathering of the Tae-Eum and Tae-Yang. On this view point, this thesis discussed the following subjects. 1. Investigate the theory of raising-falling and scattering-gathering developed in the Dongeui-Suse-Bowon. 2. Investigate the changes of the recognition of the Yang-Dog sympton and Jo-Yeol sympton argued as Interior-overheating-sympton of the Tae-Eum-In caused by liver's receiving heat. 3. Investigate the Yi-Je-Ma's view on the Eum-Yang in the argument of interior-overheating-sympton of the Tae-Eum-In caused by liver's receiving heat. As a result, the following conclusions were led to. 1. Dongeui-Suse-Bowon considers Spleen-Kidney has the couple motion of the raising Yang and falling Eum, and Liver-Lung has the couple motion of the expiratory-scattering and inspiratory-gathering. This theory of raising-falling and scattering-gathering is same as in the concept with the gathering. This theory of raising-falling and scattering-gathering is same as in the concept with the theory of raising-falling and floating-sinking of past Oriental-Medicine, but more consistently systematized in the pathology and prescription. 2. Dongeui-Suse-Bowon considers the Yang-Dog sympton and Jo-Yeol sympton as the interior-overheating-sympton of the Tae-Eum-In. As following the book, the fire of desire weeken the expiratory-scattering power of the lung, and deepen the shortage of the expiratory-scattering power comparison to the inspiratory-gathering power. Therfore the sympton can be treated by releasing ourselves from the desire and taking medicine strengthening the expiratory-scattering power. 3. In the early stage of the orintal medicine, they used prescriptions composed of So-Yang medicine and Tae-Eum medicine which can cool heat. Galgeun, Mawhang and Seungma were used in the age of Sanghanron, thereafter Jugoing's Jojung-Tang and Gongsin's Galgeunhaegi-Tang were developed as prescriptions of the interior-overheating-sympton of the Tae-Eum-In, and finally Tea-Uem-In Galgeunhaegi-Tang was settled by Yi-Je-Ma.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Tuberculosis and Respiratory Diseases
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• v.46 no.5
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• pp.685-696
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• 1999

Background : Idiopathic pulmonary fibrosis (IPF) is a diffuse inflammatory and fibrosing process that occurs within the interstitium and alveolus of the lung with invariably poor prognosis. The major problem in management of IPF results from the variable rate of disease progression and the difficulties in predicting the response to therapy. The purpose of this retrospective study was to evaluate the short-term efficacy of steroid and immunosuppressive therapy for IPF and to identify the pre-treatment determinants of favorable response. Method : Twenty patients of IPF were included. Diagnosis of IPF was proven by thoracoscopic lung biopsy and they were presumed to have active progressive disease. The baseline evaluation in these patients included clinical history, pulmonary function test, bronchoalveolar lavage (BAL), and chest high resolution computed tomography (HRCT). Fourteen patients received oral prednisolone treatment with initial dose of 1mg/kg/day for 8 to 12 weeks and then tapering to low-dose prednisolone (0.25mg/kg/day). Six patients who previously had experienced significant side effects to steroid received 2mg/kg/day of oral cyclophosphamide with or without low-dose prednisolone. Follow-up evaluation was performed after 6 months of therapy. If patients met more than one of followings, they were considered to be responders : (1) improvement of more than one grade in dyspnea index, (2) improvement in FVC or TLC more than 10% or improvement in DLco more than 20% (3) decreased extent of disease in chest HRCT findings. Result : One patient died of extrapulmonary cause after 3 month of therapy, and another patient gave up any further medical therapy due to side effect of steroid. Eventually medical records of 18 patients were analyzed. Nine of 18 patients were classified into responders and the other nine patients into nonresponders. The histopathologic diagnosis of the responders were all nonspecific interstitial pneumonia (NSIP) and that of nonresponders were all usual interstitial pneumonia (UIP) (p<0.001). The other significant differences between the two groups were female predominance (p<0.01), smoking history (p<0.001), severe grade of dyspnea (p<0.05), lymphocytosis in BAL fluid ($23.8{\pm}16.3%$ vs $7.8{\pm}3.6%$, p<0.05), and less honeycombing in chest HRCT findings (0% vs $9.2{\pm}2.3%$, p<0.001). Conclusion : Our results suggest that patients with histopathologic diagnosis of NSIP or lymphocytosis in BAL fluid are more likely to respond to steroid or immunosuppressive therapy. Clinical results in large numbers of IPF patients will be required to identify the independent variables.

• Korean Journal of Organic Agriculture
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• v.8 no.3
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• pp.111-120
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• 2000

In this study, by-products of rice bran, rice hull, bean curd dregs and food waste were fermented with different ratios of 26, 26, 34 and 14% respectively and the experimental diets were prepared mixing it with various ratios of commercial diets (fermented feed commercial feed : A 80 : 20, B 70 : 30, C 60 : 40, D 50:50). In Experiment I, feed intake, nutrients digestibilities and nitrogen retention were investigated and body weight gain in Experiment 1. For chemical composition of experimental diets, crude protein contents were 13.73, 13.78, 14.45 and 15.14% in A, B, C and D respectively and the contents of crude fat and crude fiber were highest in A as 8.66 and 27.82% respectively. 2. Dry hurler intakes were significantly higher (P<0.05) in A(362.06g/d) and C(358.49g/d) than B and D. Intakes of crude protein and crude fat were not significantly different (P>0.05) among treatments however those of crude fiber and crude ash were significantly higher (P>0.05) in A(101.47g/d). 3. Dry matter digestibilities in the range of 53.38∼68.81% in all treatments have shown the highest value in C of 60% fermented feed plus 40% commercial diet but the lowest in A of 80% of fermented feed plus 20% commercial diet (P<0.05). 59.85% of digestibility of crude protein in A was also lowest among all treatments (p<0.05), 4. 8.47g/d of nitrogen intake in C was recorded highest (P<0.05) however the highest nitrogen retention was marked in B of 50% fermented feed plus 50% of commercial diet due mainly to lower excretion of nitrogen through feces. 5. The data of live weight gain in Experiment II has not been shown as a result since the proper daily gain of body weight was achieved only in D as 88.89g/d and the goats in other treatments have shown frequent diarrhea. However, neglecting the animals with diarrhea, higher amounts of concentrates in the diets (C and D) showed the tendency of higher weight gain. 6. In this study, feeding 60% fermented feed manufactured with domestic agricultural by-products of rice bran, rice hull, bean curd dregs and food waste to Korean native goats have shown satisfactory results of intake and digestibility and it indicates that utilization of domestic agricultural by-products in goats could be improved by the process of fermentation. However it's effects on body weight gain and nitrogen retention were below than expected. Different sources of feedstuff for fermentation may result in different performances of animals. However, to draw overall conclusion from this study, 50∼60% of fermented feed can be recommended in the case of mixing with concentrates.

• Journal of Food Hygiene and Safety
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• v.26 no.4
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• pp.322-329
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• 2011

This study was done to analyze the contents of minerals and vitamins to compare the measured values of minerals, vitamins with labeled values of them in food labeling and to investigate the ratio of measured values to labeled values in 437 specimen with minerals and vitamins - fortified commercial beverages and liquid teas. Content of calcium and sodium in samples after microwave digestion was analyzed with an ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometer) and vitamins were determined using by HPLC (High Performance Liquid Chromatography). The measured values of calcium were ranged 80.3~142.6% of the labeled values in 21 samples composed calcium - fortified commercial beverages and liquid teas. In case of sodium, measured values were investigated 33.9~48.5% of the labeled values in 21 sports beverages. The measured values of vitamin C, vitamin $B_2$ and niacin were ranged 99.7~2003.6, 81.1~336.7, 90.7~393.2% of the labeled values in vitamins - fortified commercial beverages and liquid teas, 57, 12, 11 samples. To support achievement of the accurate nutrition label, there must be program and initiatives for better understanding and guidances on food labelling and nutrition for food manufacture.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.