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Erschienen in:
Online Learning of a Weighted Selective Naive Bayes Classifier with Non-convex Optimization Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

A Novel Approach to Feature Selection Based on Quality Estimation Metrics

verfasst von : Jean-Charles Lamirel, Pascal Cuxac, Kafil Hajlaoui

Erschienen in: Advances in Knowledge Discovery and Management

Verlag: Springer International Publishing

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Abstract

Feature maximization (F-max) is an unbiased quality estimation metric of unsupervised classification (clustering) that favours clusters with a maximal feature F-measure value. In this article we show that an adaptation of this metric within the framework of supervised classification allows efficient feature selection and feature contrasting to be performed. We experiment the method on different types of textual data. In this context, we demonstrate that this technique significantly improves the performance of classification methods as compared with the use of state-of-the art feature selection techniques, notably in the case of the classification of unbalanced, highly multidimensional and noisy textual data gathered in similar classes.

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Vorheriges Kapitel Comparison of Linear Modularization Criteria Using the Relational Formalism, an Approach to Easily Identify Resolution Limit
Nächstes Kapitel Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining
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Metadaten
Titel
A Novel Approach to Feature Selection Based on Quality Estimation Metrics
verfasst von
Jean-Charles Lamirel
Pascal Cuxac
Kafil Hajlaoui
Copyright-Jahr
2017
Buch
Advances in Knowledge Discovery and Management

Print ISBN: 978-3-319-45762-8

Electronic ISBN: 978-3-319-45763-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-45763-5_7