Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications

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Many pattern classification algorithms such as Support Vector Machines (SVMs), Multi-Layer Perceptrons (MLPs), and K-Nearest Neighbors (KNNs) require data to consist of purely numerical variables. However many real world data consist of both categorical and numerical variables. In this paper we suggest an effective method of converting the mixed data of categorical and numerical variables into data of purely numerical variables for binary classifications. Since the suggested method is based on the theory of learning Bayesian Network Classifiers (BNCs), it is computationally efficient and robust to noises and data losses. Also the suggested method is expected to extract sufficient information for estimating a minimum-error-rate (MER) classifier. Simulations on artificial data sets and real world data sets are conducted to demonstrate the competitiveness of the suggested method when the number of values in each categorical variable is large and BNCs accurately model the data. (C) 2009 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2010
Language
English
Article Type
Article
Keywords

SUPPORT VECTOR MACHINES

Citation

COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.54, no.5, pp.1247 - 1265

ISSN
0167-9473
DOI
10.1016/j.csda.2009.11.003
URI
http://hdl.handle.net/10203/94222
Appears in Collection
RIMS Journal Papers
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