TITLE

Classification with Mixed Numeric and Categorical Data Using Improved Extension Theory

AUTHOR(S)
Cheng-Hsiang Liu
PUB. DATE
January 2013
SOURCE
International MultiConference of Engineers & Computer Scientists;2013, Vol. 1, p1
SOURCE TYPE
Conference Proceeding
DOC. TYPE
Article
ABSTRACT
Classification is a chief issue in decision science and knowledge discovery, hence the recent development of several classification methods. Compared to other methods, extension theory (ET) does not require a particular learning process, and its calculation is both fast and simple. This paper proposes improved extension theory (IET) to supersede the shortcoming of ET, such as failure to implement data classification when attributes are categorical. This study assesses IET performance according to six real-world datasets. Comparisons with other classifiers (i.e., ET and decision trees) illustrate the effectiveness of the proposed IET.
ACCESSION #
97333841

 

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