NDLI: Kernel-based linear classification on categorical data

Content Provider	Springer Nature Link
Author	Chen, Lifei Ye, Yanfang Guo, Gongde Zhu, Jianping
Copyright Year	2015
Abstract	Kernel-based methods have been widely investigated in the soft-computing community. However, they focus mainly on numeric data. In this paper, we propose a novel method for kernel learning on categorical data, and show how the method can be used to derive effective classifiers for linear classification. Based on kernel density estimation for categorical attributes, three popular classification methods, i.e., Naive Bayes, nearest neighbor and prototype-based classification, are effectively extended to classify categorical data. We also propose two data-driven approaches to the bandwidth selection problem, with one aimed at minimizing the mean squared error of the kernel estimate and the other endeavored to attribute weights optimization. Theoretical analysis indicates that, as in the numeric case, kernel learning of categorical attributes is capable to make the classes to be more separable, resulting in outstanding performances of the new classifiers on various real-world data sets.
Starting Page	2981
Ending Page	2993
Page Count	13
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	8
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-11-05
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data classification Categorical attributes Kernel density estimation Naive Bayes Nearest neighbor Prototype-based classification Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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