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Relevance popularity: A term event model based feature selection scheme for text classification

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Publicat a:PLoS One vol. 12, no. 4 (Apr 2017), p. e0174341
Autor principal: Feng, Guozhong
Altres autors: An, Baiguo, Yang, Fengqin, Wang, Han, Zhang, Libiao
Publicat:
Public Library of Science
Matèries:
China
Text categorization
Word sense disambiguation
Bayesian analysis
Computer science
Information storage
Bioinformatics
Studies
Classification
Neural networks
Relevance
Economic
Information processing
Methods
Algorithms
Text editing
Information theory
Information technology
Information retrieval
Numerical experiments
Artificial intelligence
Laboratories
Statistical analysis
Probabilistic models
Datasets
Support vector machines
Documents
Accés en línia:Citation/Abstract
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Resum:Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.
ISSN:1932-6203
DOI:10.1371/journal.pone.0174341
Font:Health & Medical Collection