Extension of ICF classifiers to real world data sets

Authors:
Kazuya Haraguchi;Hiroshi Nagamochi
Affiliations:
Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Japan;Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Japan
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

Classification problem asks to construct a classifier from a given data set, where a classifier is required to capture the hidden oracle of the data space. Recently, we introduced a new class of classifiers ICF, which is based on iteratively composed features on {0, 1, *}-valued data sets. We proposed an algorithm ALG-ICF* to construct an ICF classifier and showed its high performance. In this paper, we extend ICF so that it can also process real world data sets consisting of numerical and/or categorical attributes. For this purpose, we incorporate a discretization scheme into ALG-ICF* as its preprocessor, by which an input real world data set is transformed into {0, 1, *}-valued one. Based on the experimental studies on conventional discretization schemes, we propose a new discretization scheme, integrated construction (IC). Our computational experiments reveal that the ALG-ICF* equipped with IC outperforms a decision tree constructor C4.5 in many cases.