• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.619-626
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• 2007

Constructive induction is a technique to draw useful attributes from given primitive attributes to classify given examples more efficiently. Useful attributes are obtained from given primitive attributes by applying appropriate operators to them. The paper proposes a constructive induction approach for a GA-based inductive learning environment that learns classification rules that ate similar to rules used in PROSPECTOR from given examples. The paper explains our constructive induction approach in details, centering on operators to combine primitive attributes and methods to evaluate the usefulness of derived attributes, and presents the results of various experiments performed to evaluate the effect of our constructive induction approach on the GA-based learning environment.

• Journal for History of Mathematics
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• v.31 no.3
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• pp.151-166
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• 2018

The rule of the game of Chomp is simple and the existence of a winning strategy can easily be proved. However, the existence tells us nothing about what strategies are winning in reality. Like in Chess or Baduk, many researchers studied the winning moves using computer programs, but no general patterns for the winning actions have not been found. In the paper, we aim to construct practical winning strategies based on backward induction. To do this we develop how to analyze Chomp and prove and find the winning strategies of the simple games of Chomp.

• Genomics & Informatics
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• v.14 no.4
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.144-154
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• 2003

In this paper we present Logical Evolution method which is a new teaming algorithm for the concepts expressed as binary logic function. We try to solve some problems of Inductive Learning algorithms through Logical Evolution. First, to be less affected from limited prior knowledge, it generates features using the gained informations during learning process and learns the concepts with these features. Second, the teaming is done using not the whole example set but the individual example, so even if new problem or new input-output variables are given, it can use the previously generated features. In some cases these old features can make the teaming process more efficient. Logical Evolution method consists of 5 operations which are selected and performed by the logical evaluation procedure for feature generation and learning process. To evaluate the performance of the present algorithm, we make experiments on MONK data set and a newly defined problem.