• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.407-410
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine teaming, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four data sets with good performance results

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.539-551
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• 2004

Multiple responses are often observed in many application fields, such as customer's time-of-day pattern for using internet. Some decision trees for multiple responses have been constructed by many researchers. However, if the response is a high-dimensional vector that can be thought of as a discretized function, then fitting a multivariate decision tree may be unsuccessful. Yu and Lambert (1999) suggested spline tree and principal component tree to analyze high dimensional response vector by using dimension reduction techniques. In this paper, we shall propose factor tree which would be more interpretable and competitive. Furthermore, using Korean internet company data, we will analyze time-of-day patterns for internet user.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.272-278
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• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.

• 한국정보시스템학회지:정보시스템연구
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• 제28권3호
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• pp.249-276
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• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권3호
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• pp.156-166
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• 2002

결정트리 생성은 특징값들로 기술된 사례들로부터 분류 규칙을 추출하는 유용한 기계학습 방법중 하나이다. 결정트리는 특징공간을 분할하는 형태에 따라 단변수(univariate) 결정트리와 다변수(multivariate) 결정트리로 대별된다. 실제 현장에서 얻어지는 데이터는 관측오류, 불확실성, 주관적인 판단 등의 이유로 특징값 자체에 오류를 포함하는 경우가 많다. 이러한 오류에 대해 강건한 결정트리를 생성하기 위한 방법으로 퍼지 기법을 도입한 결정트리 생성 방법에 대한 연구가 진행되어 왔다. 현재까지 대부분의 퍼지 결정트리에 대한 연구는 단변수 결정트리에 퍼지 기법을 도입한 것들이며, 다변수 결정트리에 퍼지 기법을 적용한 것은 찾아보기 힘들다. 이 논문에서는 다변수 결정트리에 퍼지 기법을 적용하여 퍼지사선형 결정트리라고 하는 퍼지 결정트리를 생성하는 방법을 제안한다. 또한 제안한 결정트리 생성 방법의 특성을 보이기 위한 실험 결과를 보인다.

• 인터넷정보학회논문지
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• 제12권6호
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• pp.63-71
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• 2011

심전도 질환 데이터는 일반적으로 분류기를 사용한 실험이 많다. 심전도 신호는 QRS-Complex와 R-R interval을 추출하는 경우가 많은데 본 실험에서는 R-R interval을 추출하여 실험하였다. 심전도 데이터의 분류기 실험은 일반적으로 SVM(Support Vector Machine)과 MLP(Multilayer Perceptron) 분류기로 수행되지만 본 실험은 정확도 향상을 위해 Random Forest 분류기 알고리즘 중 Decision Tree를 Best-First Decision Tree(B-F Tree)로 수정하여 실험하였다. 그리고 정확도 비교분석을 위해 SVM, MLP, RBF(Radial Basic Function) Network와 Decision Tree 분류기 실험을 같이 수행하였고, 동일한 데이터와 간격으로 실험한 타 논문의 결과와 비교해보았다. 수정한 Random Forest 분류기의 정확도를 다른 네 개의 분류기와 타 논문의 실험과 비교해보니 정확도 부분에서는 Random Forest가 가장 우수하였다. 본 실험의 전처리 과정은 대역통과 필터(Band-pass filter)를 사용하여 R-R interval을 추출하였는데 향후에는 정확한 간격을 추출하기 위한 필터의 연구가 사려된다.

• 대한산업공학회지
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• 제30권4호
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• pp.267-276
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• 2004

Much of previous attention on researches of the decision tree focuses on the splitting criteria and optimization of tree size. Nowadays the quantity of the data increase and relation of variables becomes very complex. And hence, this comes to have plenty number of unnecessary node and leaf. Consequently the confidence of the explanation and forecasting of the decision tree falls off. In this research report, we propose some decision tree algorithms considering the interaction of predictor variables. A generic algorithm, the k-1 Algorithm, dealing with the interaction with a combination of all predictor variable is presented. And then, the extended version k-k Algorithm which considers with the interaction every k-depth with a combination of some predictor variables. Also, we present an improved algorithm by introducing control parameter to the algorithms. The algorithms are tested by real field credit card data, census data, bank data, etc.

• 한국한의학연구원논문집
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• 제14권3호
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• pp.81-87
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• 2008

Sasanag Contitution Medicine(SCM) is the traditional medicine theory based on constitutional medicine in Korea. It is most import ant that a personal SCM type is determined accurately ahead of applying any Sasang treatments. For this, many researches have been studied to diagnose the SCM type using constitutional clinical data. The decision tree is a tree-structured data-mining methodology. Recently, in the Korean traditional medicine society, there have been several efforts to find diagnosing tools using the decision tree method. So, we developed a decision tree program based on web for analyzing constitutional clinical information. It can use various clinical data as input data, offer filtering function to select clinical data to be used. We can find useful factor to be influential on SCM types using this program.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.823-829
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• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.