• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.489-506
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• 2007

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated structures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. There are Ab Initio method, Similarity-based method, and Ensemble method for gene prediction method for eukaryotic genes. Each Method use various algorithms. This paper introduce how to predict genes using HMM(Hidden Markov Model) algorithm and present the process of gene prediction with well-known gene prediction programs.

• Journal of Microbiology
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• v.44 no.2
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• pp.137-144
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• 2006

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.730-735
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• 2006

This paper proposes a time series prediction method for the short term electrical load will) the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction system may be obtained. We obtain good results for the time prediction of the short term electric load by the proposed algorithm. In addition we implement the graphic user interface for the proposed algorithms. Finally, we implement the regional prediction system for the electric load.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.