Feature selection for multi-label naive Bayes classification

Min-Ling Zhang; José M. Peña; Vı́ctor Robles

doi:10.1016/j.ins.2009.06.010

ScienceGate Book Chapters

JOURNAL ARTICLE

Feature selection for multi-label naive Bayes classification

Min-Ling Zhang José M. Peña Vı́ctor Robles

Year: 2009 Journal: Information Sciences Vol: 179 (19)Pages: 3218-3229 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2009.06.010

Get Full-Text PDF Get Analytical Report

Keywords:

Artificial intelligence Computer science Naive Bayes classifier Feature selection Machine learning Multi-label classification Pattern recognition (psychology) Principal component analysis Feature (linguistics) Set (abstract data type) Selection (genetic algorithm) Task (project management) Data mining Support vector machine

Metrics

493

Cited By

14.86

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Rough Sets and Fuzzy Logic

Physical Sciences → Computer Science → Computational Theory and Mathematics

Image Retrieval and Classification Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Feature selection for multi-label naive Bayes classification

Metrics

Citation History

Topics

Related Documents

Robust Method of Sparse Feature Selection for Multi-Label Classification with Naive Bayes

Feature subset selection using naive Bayes for text classification

A Double Weighted Naive Bayes for Multi-label Classification

A correlation-based feature weighting filter for multi-label Naive Bayes

Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection