A Multi-criteria Decision Making Approach
In Feature Selection For Enhancing
Text Categorization
Author(s)
S. Doan & S. Horiguchi
Abstract
This paper considers the problem of feature selection in text categorization. Previous
works in feature selection often used a filter model in which features, after
ranked by a measure, are selected based on a given threshold. In this paper, we
present a novel approach to feature selection based on multi-criteria decision making
of each feature. Instead of only one criterion, multi-criteria of a feature are
used; and a procedure based on each threshold of the criterion is proposed. This
framework seems to be suitable for text data and can be applied to feature selection
in text categorization. Experimental results on Reuters-21578 benchmark data
show that our approach has a promising scheme and enhances the performance of
a text categorization system.
1 Introduction
Feature selection is an interesting issue recently in machine learning as well as
data mining communities [1, 2, 3]. Up to now, there has been two most common
approaches: the filter and the wrapper [4, 5]. Both approaches use prior knowledge
as the heuristics indicator for selecting the optimal feature subset. The filter
approach uses measurements of features as the criterion. In [6], Huang listed
four measurements of features belonging to the filtering approach, that is information,
distance, dependence and consistency measures. Based on a measurement,
the optimal subset of features is chosen by filtering the \“noise” or \“irrelevant” features.
Apart from the filter model, the wrapper approach based on the criterion of
Keywords
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