• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.845-857
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• 2003

We explore the use of genetic programming to evolve decision trees directly for classification problems with both discrete and continuous predictors. We demonstrate that the derived hypotheses of standard algorithms can substantially deviated from the optimum. This deviation is partly due to their top-down style procedures. The performance of the system is measured on a set of real and simulated data sets and compared with the performance of well-known algorithms like CHAID, CART, C5.0, and QUEST. Proposed algorithm seems to be effective in handling problems caused by top-down style procedures of existing algorithms.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.2
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• pp.24-34
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• 1994

Scientific involvement in complex decision-making system, characterized by multicriteria phenomena and fuzziness inherent in the structure of information, requires suitable methods. Especially, when powerful dependent criteria are introduced and their weighted value structure is ignorant, the systems are become more complex. This paper presents a fuzzy dependenced relation model and fuzzy measure model for this kind of multicriteria decision-making. The model we propose is based on fuzzy relation and fuzzy measure in fuzzy systems theory. For the application of the model, a numdrical example is quoted.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.742-748
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• 1994

Scientific involvement in complex decision-making systems, characterized by multicriteria phenomena and fuzziness inherent in the structure of information, requires suitable methods. Especially, when powerful dependent criteria are introduced, the systems are become more complex. This paper presents a fuzzy dependence relation model for this kind of multicriteria decision-making. The model we propose is based on fuzzy relation in fuzzy system theory. For the application of the model a numerical example is quoted.

• Journal of applied mathematics & informatics
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• v.38 no.3_4
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• pp.335-349
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• 2020

We present inverse soft rough sets by using inverse soft sets and soft rough sets. We study different approaches for inverse soft rough set and examine the relationships between them. We also discuss and explore the basic properties for these approaches. Moreover we develop an algorithm following these concepts and apply it to a decision-making problem to demonstrate the applicability of the proposed methods.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.937-943
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• 2000

Data mining usually deal with undesigned massive data containing many variables for which their characteristics and association rules are unknown, therefore it is actually not easy to interpret the results of analysis. In this paper, it is shown that decision tree can be very useful in interpreting data mining prediction model using two real examples.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.411-423
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• 2002

In this paper, we propose a split criterion for binary decision trees. The proposed criterion selects the optimal split by measuring the prediction success of the candidate splits at a given node. The criterion is shown to have the property of exclusive preference. Examples are given to demonstrate the properties of the criterion.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.97-112
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• 1991

This paper describes two contrasting approaches to group decision making involving multiple criteria. A compromise programming method and the analytic hierarchy process are analyzed and compared by using an illustrative example of a computer model selection problem to demonstrate their usefulness as a viable tool for group decision making. This paper further considers some extensions and modifications of there two methods for future study.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.265-266
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• 2007

New coding tools require the increase of the encoder complexity in H.264. In this paper we propose a fast mode decision method to reduce the computational complexity of mode decision. The simulation results shows that the proposed methods could reduce the coding time of overall sequences by 30% on average without any noticeable degradation of the coding efficiency.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.449-466
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• 2006

We propose to use variable selection methods based on penalized regression for pruning decision tree ensembles. Pruning methods based on LASSO and SCAD are compared with the cluster pruning method. Comparative studies are performed on some artificial datasets and real datasets. According to the results of comparative studies, the proposed methods based on penalized regression reduce the size of boosting ensembles without decreasing accuracy significantly and have better performance than the cluster pruning method. In terms of classification noise, the proposed pruning methods can mitigate the weakness of AdaBoost to some degree.