INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION

INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION

碩士 === 大同大學 === 資訊經營學系(所) === 99 === Feature selection has been proved to be very important for classification. There are some statistical approaches, such as Information Gain, Mutual Information, andχ2, etc. The features are measured one by one, therefore the inference of combination of features is...

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Main Authors: Li-Wen Lin, 林莉雯
Other Authors: Yen-Ju Yang
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/59314007796932477306
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spelling ndltd-TW-099TTU057160412015-10-13T20:27:49Z http://ndltd.ncl.edu.tw/handle/59314007796932477306 INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION 整合二進制粒子群最佳化與遺傳演算法之特徵選擇於文件分類 Li-Wen Lin 林莉雯 碩士 大同大學 資訊經營學系(所) 99 Feature selection has been proved to be very important for classification. There are some statistical approaches, such as Information Gain, Mutual Information, andχ2, etc. The features are measured one by one, therefore the inference of combination of features is not considered. In recent years, the evolution-based computing algorithms have been involved in feature selection to search the best combination of features. This research presents a feature selection algorithm integrating Binary Particle Swarm Optimization and Genetic Algorithm for Text Classification. The objective is to find the global optimal features for high dimensional data classification, especially for text classification. Yen-Ju Yang 楊燕珠 2011 學位論文 ; thesis 37 zh-TW
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language zh-TW
format Others
sources NDLTD
description 碩士 === 大同大學 === 資訊經營學系(所) === 99 === Feature selection has been proved to be very important for classification. There are some statistical approaches, such as Information Gain, Mutual Information, andχ2, etc. The features are measured one by one, therefore the inference of combination of features is not considered. In recent years, the evolution-based computing algorithms have been involved in feature selection to search the best combination of features. This research presents a feature selection algorithm integrating Binary Particle Swarm Optimization and Genetic Algorithm for Text Classification. The objective is to find the global optimal features for high dimensional data classification, especially for text classification.
author2 Yen-Ju Yang
author_facet Yen-Ju Yang
Li-Wen Lin
林莉雯
author Li-Wen Lin
林莉雯
spellingShingle Li-Wen Lin
林莉雯
INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION
author_sort Li-Wen Lin
title INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION
title_short INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION
title_full INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION
title_fullStr INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION
title_full_unstemmed INTEGRATING BINARY PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM FOR FEATURE SELECTION IN TEXT CLASSIFICATION
title_sort integrating binary particle swarm optimization and genetic algorithm for feature selection in text classification
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/59314007796932477306
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