• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.215-228
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• 2003

Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, interaction effect identification, category merging and discretizing continuous variable, etc. CHAID(Chi-square Automatic Interaction Detector), is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. CHAID modeling selects a set of predictors and their interactions that optimally predict the dependent measure. In this paper we explore CHAID algorithm in view of accuracy and speed by sampling proportion.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.142-150
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• 2002

This study describes a method of finding an optimal path of a soccer robot by using a concentric circle method with different radii of rotation. Comparing with conventional algorithms which try to find the shortest path length, the variable concentric circle method find the shortest moving time. The radius fur the shortest moving time for a given ball location depends on the relative location between a shooting robot and a ball. Practically it is difficult to find an analytical solution due to many unknowns. Assuming a radius of rotation within a possible range, total path moving time can be calculated by adding the times needed for straight path and circular path. Among these times the shortest time is obtained. In this paper, a graphical solution is presented such that the game ground is divided into 3 regions with a minimum, medium, and maximum radius of rotation.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.299-307
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• 2012

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Journal of Ship and Ocean Technology
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• v.5 no.1
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• pp.30-37
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• 2001

Ship optimal design is a multi-objective decision-making process and its optimal solution does not exit in general. It is a problem in which the decision-maker is very interested that an effective solution is how to be found which has good characteristic and is substituted for optimal solution in a sense. In the previous methods of multi-objective decision-making, the weighting coefficients are decided from the point of view of individuals which have a bit sub-jective an unilateral behavior. in order to fairly and objectively decide the weighting coeffi-cients, which are considered to be optimal in all system of multi-objective decision-making and satisfactory solution to the decision-maker, the pater presents a method of applying the Technology of the Biggest Entropy. It is proved that the method described in the paper is very feasible and effective be means of a practical example of ship optimal design.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.189-195
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• 2014

In this paper, we conducted a study on place-free and time-free cloud computing (CC) adoption decision-making model. Panel survey data which is collected from 65 people and CART (classification and regression tree) which is one of data mining approaches are used to construct decision-making model. In this modeling, there are 2 steps: In the first step, significant questions (variables) are selected. After that, the CART decision-making model is constructed using the selected variables. In the variable selection stage, the 25 questions are reduced to 5 ones. The benefits of question reduction are quick response from respondent and reducing model-construction time.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.471-478
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• 2003

In this paper, we reconsider the Kolmogorov-Smirnoff distance as a split criterion for binary decision trees and suggest an algorithm to obtain the Kolmogorov-Smirnoff distance more efficiently when the input variable have more than three categories. The Kolmogorov-Smirnoff distance is shown to have the property of exclusive preference. Empirical results, comparing the Kolmogorov-Smirnoff distance to the Gini index, show that the Kolmogorov-Smirnoff distance grows more accurate trees in terms of misclassification rate.

• Journal of the Korean Operations Research and Management Science Society
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• v.7 no.1
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• pp.51-57
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• 1982

During the course of history, more and more reliable birth control methods have become available. Hence, to a certain degree, the possibility of avoiding any or additional children, and of spacing the family building process has arisen. The advancement of six predetermination technology, whereby couples can influence the sex of their children, gives couples, another decision variable. Assuming a rational acting couple, we present a general decision-theoretic model which describes the family building process and its optimization through maximizing the expected utility concerning the spacing, ordering, sex, and number of their children.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.265-268
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• 2002

For accurate interpretation, ECG signal is measured by using 12 leads method. We look shape of Measured ECG signal and decide whether interpretation is accurate or not. In this paper, we propose new effective fuzzy decision system which uses fuzzy rules and membership functions for more accurate of ECG wave. We used PR interval, QRS interval and QRS axis as conditional variables for designing fuzzy rules. And decision rule of conclusion variable is determined by (sinus rhythm), (sinus rhythm+left deviation), (sinus rhythm+right deviation) and (sinus rhythm+negative axis). Experimental results showed our system made numerically easy decision possible and had advantage of simple design method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.827-833
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• 2020

In this paper, to reduce the unpaid rate of local governments, the internal data elements affecting the arrears in Water-INFOS are searched through interviews with meter readers in certain local governments. Candidate data affecting arrears from national statistical data were derived. The influence of the independent variable on the dependent variable was sampled by examining the disorder of the dependent variable in the data set called information gain. We also evaluated the higher prediction rates of decision tree and logistic regression using n-fold cross-validation. The results confirmed that the decision tree can find more accurate customer payment patterns than logistic regression. In the process of developing an analysis algorithm model using machine learning, the optimal values of two environmental variables, the minimum number of data and the maximum purity, which directly affect the complexity and accuracy of the decision tree, are derived to improve the accuracy of the algorithm.