Authors:
Artur Ferreira
1
and
Mario Figueiredo
2
Affiliations:
1
Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, Portugal
;
2
Instituto Superior Técnico and Instituto de Telecomunicações, Portugal
Keyword(s):
Feature discretization, Feature selection, Static discretization, Dynamic discretization, Wrapper approach, Linde-Buzo-Gray algorithm.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Feature Selection and Extraction
;
Pattern Recognition
;
Theory and Methods
Abstract:
In many learning problems, an adequate (sometimes discrete) representation of the data is necessary. For instance,
for large number of features and small number of instances, learning algorithms may be confronted
with the curse of dimensionality, and need to address it in order to be effective. Feature selection and feature
discretization techniques have been used to achieve adequate representations of the data, by selecting an
adequate subset of features with a convenient representation. In this paper, we propose static and dynamic
methods for feature discretization. The static method is unsupervised and the dynamic method uses a wrapper
approach with a quantizer and a classifier, and it can be coupled with any static (unsupervised or supervised)
discretization procedure. The proposed methods attain efficient representations that are suitable for learning
problems. Moreover, using well-known feature selection methods with the features discretized by our methods
leads to better accur
acy than with the features discretized by other methods or even with the original features.
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