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An Incremental Method Using Sample Split Points for Global Discretization  

한경식 ((주)인우기술)
이수원 (숭실대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.7, 2004 , pp. 849-858 More about this Journal
Abstract
Most of supervised teaming algorithms could be applied after that continuous variables are transformed to categorical ones at the preprocessing stage in order to avoid the difficulty of processing continuous variables. This preprocessing stage is called global discretization, uses the class distribution list called bins. But, when data are large and the range of the variable to be discretized is very large, many sorting and merging should be performed to produce a single bin because most of global discretization methods need a single bin. Also, if new data are added, they have to perform discretization from scratch to construct categories influenced by the data because the existing methods perform discretization in batch mode. This paper proposes a method that extracts sample points and performs discretization from these sample points in order to solve these problems. Because the approach in this paper does not require merging for producing a single bin, it is efficient when large data are needed to be discretized. In this study, an experiment using real and synthetic datasets was made to compare the proposed method with an existing one.
Keywords
Global Discretization; Machine Learning; Incremental Learning; Large Dataset; Data Mining;
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Times Cited By KSCI : 1  (Citation Analysis)
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