• Title/Summary/Keyword: Decision-tree technique

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Heuristic Approach to Service Restoration (경험 지식 기반 정전 복구)

  • Kim, Jong-Boo;Choi, Sang-Yule;An, Bi-O
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.1019-1020
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    • 2006
  • The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to identify the most effective the set of switches using proposed search technique and a feeder load balance index. Test results on the KEPCO's 108 bus distribution system show that the performance is efficient and robust

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Decision-making system for the resource forecasting and risk management using regression algorithms (회귀알고리즘을 이용한 자원예측 및 위험관리를 위한 의사결정 시스템)

  • Han, Hyung-Chul;Jung, Jae-Hun;Kim, Sin-Ryeong;Kim, Young-Gon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.15 no.6
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    • pp.311-319
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    • 2015
  • In this paper, in order to increase the production efficiency of the industrial plant, and predicts the resources of the manufacturing process, we have proposed a decision-making system for resource implementing the risk management effectively forecasting and risk management. A variety of information that occurs at each step efficiently difficult the creation of detailed process steps in the scenario you want to manage, is a frequent condition change of manufacturing facilities for the production of various products even within the same process. The data that is not contiguous products production cycle also not constant occurs, there is a problem that needs to check the variation in the small amount of data. In order to solve these problems, data centralized manufacturing processes, process resource prediction, risk prediction, through a process current status monitoring, must allow action immediately when a problem occurs. In this paper, the range of change in the design drawing, resource prediction, a process completion date using a regression algorithm to derive the formula, classification tree technique was proposed decision system in three stages through the boundary value analysis.

Development of Cartographic Models of Openspace Management for Practical Use of GIS (GIS를 활용한 녹지관리 지도모델의 개발)

  • Gwak, Haeng-Goo;Cho, Young-Hwan
    • Journal of Korean Society for Geospatial Information Science
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    • v.5 no.2 s.10
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    • pp.45-54
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    • 1997
  • A mathodology to manage effectively urban open space using GIS(Geographic Information System) was developed to explore the methology of efficient urban open space management focusing on landscaped trees. Cartographic modeling technique was used for practical use of GIS as a case study of the Childeren's park in Kwangju city. First, spatial and attribute information for efficient landscaped tree management was acqired through the development of a tree management cartographic model. Second the information of location and the attribute of individual trees can be applied as a means of decision making in tree management. Thira optimal path of tree management and priority of management in work process of the selected urban open space could be determined according to the objective of park management.

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Recommendation of Optimal Treatment Method for Heart Disease using EM Clustering Technique

  • Jung, Yong Gyu;Kim, Hee Wan
    • International Journal of Advanced Culture Technology
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    • v.5 no.3
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    • pp.40-45
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    • 2017
  • This data mining technique was used to extract useful information from percutaneous coronary intervention data obtained from the US public data homepage. The experiment was performed by extracting data on the area, frequency of operation, and the number of deaths. It led us to finding of meaningful correlations, patterns, and trends using various algorithms, pattern techniques, and statistical techniques. In this paper, information is obtained through efficient decision tree and cluster analysis in predicting the incidence of percutaneous coronary intervention and mortality. In the cluster analysis, EM algorithm was used to evaluate the suitability of the algorithm for each situation based on performance tests and verification of results. In the cluster analysis, the experimental data were classified using the EM algorithm, and we evaluated which models are more effective in comparing functions. Using data mining technique, it was identified which areas had effective treatment techniques and which areas were vulnerable, and we can predict the frequency and mortality of percutaneous coronary intervention for heart disease.

A Study-on Context-Dependent Acoustic Models to Improve the Performance of the Korea Speech Recognition (한국어 음성인식 성능향상을 위한 문맥의존 음향모델에 관한 연구)

  • 황철준;오세진;김범국;정호열;정현열
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.4
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    • pp.9-15
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    • 2001
  • In this paper we investigate context dependent acoustic models to improve the performance of the Korean speech recognition . The algorithm are using the Korean phonological rules and decision tree, By Successive State Splitting(SSS) algorithm the Hidden Merkov Netwwork(HM-Net) which is an efficient representation of phoneme-context-dependent HMMs, can be generated automatically SSS is powerful technique to design topologies of tied-state HMMs but it doesn't treat unknown contexts in the training phoneme contexts environment adequately In addition it has some problem in the procedure of the contextual domain. In this paper we adopt a new state-clustering algorithm of SSS, called Phonetic Decision Tree-based SSS (PDT-SSS) which includes contexts splits based on the Korean phonological rules. This method combines advantages of both the decision tree clustering and SSS, and can generated highly accurate HM-Net that can express any contexts To verify the effectiveness of the adopted methods. the experiments are carried out using KLE 452 word database and YNU 200 sentence database. Through the Korean phoneme word and sentence recognition experiments. we proved that the new state-clustering algorithm produce better phoneme, word and continuous speech recognition accuracy than the conventional HMMs.

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Loss Reduction in Heavy Loaded Distribution Networks Using Cyclic Sub Tree Search (순환적 부분트리 탐색법을 이용한 중부하 배전계통의 손실최소화)

  • Choi, Sang-Yule;Shin, Myong-Chul
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.50 no.5
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    • pp.241-247
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    • 2001
  • Network reconfiguration in distribution systems is realized by changing the status of sectionalizing switches, and is usually done for loss reduction of load balancing in the system. This paper presents an effective heuristic based switching scheme to solve the distribution feeder loss reduction problem. The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to apply a proposed technique called cyclic best first search. the proposed algorithm identify the most effective the set of switch status configuration of distribution system for loss reduction. To demonstrate the validity of the proposed algorithm, numerical calculations are carried out the 32, 69 bus system models.

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Decision of Optimum Grinding Condition by Pass Schedule Change (열간압연 스케줄변경에 따른 최적연삭조건 결정)

  • Bae, Yong-Hwan
    • Journal of the Korean Society of Safety
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    • v.23 no.6
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    • pp.7-13
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    • 2008
  • It is important to prevent roll failure in hot rolling process for reducing maintenance cost and production loss. The relationship between rolling pass schedule and the work roll wear profile will be presented. The roll wear pattern is related with roll catastrophic failure. The irregular and deep roll wear pattern should be removed by On-line Roll Grinder(ORG) for roll failure prevention. In this study, a computer roll wear prediction model under real process working condition is developed and evaluated with hot rolling pass schedule. The method of building wear calculation functions for center portion abrasion and marginal abrasion respectively was used to develop a work roll wear prediction mathematical model. The three type rolling schedule are evaluated by wear prediction model. The optimum roll grinding methods is suggested for schedule tree rolling technique.

The Difference Analysis between Maturity Stages of Venture Firms by Classification Techniques of Big Data (빅데이터 분류 기법에 따른 벤처 기업의 성장 단계별 차이 분석)

  • Jung, Byoungho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.15 no.4
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    • pp.197-212
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    • 2019
  • The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

Imbalanced Data Improvement Techniques Based on SMOTE and Light GBM (SMOTE와 Light GBM 기반의 불균형 데이터 개선 기법)

  • Young-Jin, Han;In-Whee, Joe
    • KIPS Transactions on Computer and Communication Systems
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    • v.11 no.12
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    • pp.445-452
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    • 2022
  • Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

Method for Assessing Landslide Susceptibility Using SMOTE and Classification Algorithms (SMOTE와 분류 기법을 활용한 산사태 위험 지역 결정 방법)

  • Yoon, Hyung-Koo
    • Journal of the Korean Geotechnical Society
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    • v.39 no.6
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    • pp.5-12
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    • 2023
  • Proactive assessment of landslide susceptibility is necessary for minimizing casualties. This study proposes a methodology for classifying the landslide safety factor using a classification algorithm based on machine learning techniques. The high-risk area model is adopted to perform the classification and eight geotechnical parameters are adopted as inputs. Four classification algorithms-namely decision tree, k-nearest neighbor, logistic regression, and random forest-are employed for comparing classification accuracy for the safety factors ranging between 1.2 and 2.0. Notably, a high accuracy is demonstrated in the safety factor range of 1.2~1.7, but a relatively low accuracy is obtained in the range of 1.8~2.0. To overcome this issue, the synthetic minority over-sampling technique (SMOTE) is adopted to generate additional data. The application of SMOTE improves the average accuracy by ~250% in the safety factor range of 1.8~2.0. The results demonstrate that SMOTE algorithm improves the accuracy of classification algorithms when applied to geotechnical data.