• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.54-59
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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 learning, 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 these data.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.205-213
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• 2018

Hyperspectral image is more increasing spectral resolution that Multi-spectral image. Because of that, each pixel of the hyperspectral image includes much more information and it is considered the most appropriate technic for land cover classification. but recent research of hyperspectral image is stayed land cover classification of general level. therefore we classified land cover of detail level using ED, SAM, SSS method and made Decision Tree from result of that. As a result, the overall accuracy of general level was improved by 1.68% and the overall accuracy of detail level was improved by 5.56%.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.347-357
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• 2004

It is very important to select a split variable in constructing the classification tree. The efficiency of a classification tree algorithm can be evaluated by the variable selection bias and the variable selection power. The C4.5 has largely biased variable selection due to the influence of many distinct values in variable selection and the QUEST has low variable selection power when a continuous predictor variable doesn't deviate from normal distribution. In this thesis, we propose the SRT algorithm which overcomes the drawback of the C4.5 and the QUEST. Simulations were performed to compare the SRT with the C4.5 and the QUEST. As a result, the SRT is characterized with low biased variable selection and robust variable selection power.

• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Genomics & Informatics
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• v.2 no.3
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• pp.121-125
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• 2004

The purpose of this study was to investigate the use of decision tree for the classification of antimicrobial peptides. The classification was based on the activities of known antimicrobial peptides against common microbes including Escherichia coli and Staphylococcus aureus. A feature selection was employed to select an effective subset of features from available attribute sets. Sequential applications of decision tree with 17 nodes with 9 leaves and 13 nodes with 7 leaves provided the classification rates of $76.74\%$ and $74.66\%$ against E. coli and S. aureus, respectively. Angle subtended by positively charged face and the positive charge commonly gave higher accuracies in both E. coli and S. aureusdatasets. In this study, we describe a successful application of decision tree that provides the understanding of the effects of physicochemical characteristics of peptides on bacterial membrane.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.201-214
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• 2014

In this paper, we propose a test case generation method adequate to destructive test of the BLDC(Brush Less Direct Current) motor controller used for the MDPS(Motor Driven Power Steering) system embedded in an automobile. The proposed method is a modified CTM(Classification Tree Method). CTM generates test cases assuming that all inputs are equally important. Therefore, it is very hard to generate test cases for extreme situations. To overcome the drawback and generate test cases specialized for destructive test. a modified CTM that compensates the limitation of traditional CTM is proposed. The proposed method has an advantage that it can intensively generate the test scenarios adequate to extreme situations by combining the test cases generated by the transitional CTM the while keeping the merit of the traditional CTM. The test scenarios for destructive test for the MDPS system embedded in a commercial automobile are generated utilizing the proposed method. The effectiveness of the proposed algorithm is verified through the test.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1365-1371
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.238-245
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• 2013

Gesture recognition has been widely one of the research areas for natural user interface. This paper presents a novel gesture recognition method using tree classification and multiclass SVM(Support Vector Machine). In the learning step, 3D trajectory of human gesture obtained by a Kinect sensor is classified into the tree nodes according to their distributions. The gestures are resampled and we obtain the histogram of the chain code from the normalized data. Then multiclass SVM is applied to the classified gestures in the node. The input gesture classified using the constructed tree is recognized with multiclass SVM.