A review on feature selection and feature extraction for text classification

机译：文本分类的特征选择和特征提取综述

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摘要

Day by day the number of text documents in digital form is increasing. Text classification is used to organize these text documents. However, text classification has the problem of high dimensionality of feature space. This high dimensionality of feature space is solved by feature selection and feature extraction methods and improves the performance of text categorization. The feature selection and feature extraction techniques remove the irrelevant features from the text documents and reduce the dimensionality of feature space. This paper presents the various feature selection and feature extraction methods. This paper also discusses various classifiers to classify the documents.

机译：数字形式的文本文档的数量每天都在增加。文本分类用于组织这些文本文档。但是，文本分类存在特征空间的维数高的问题。特征空间的这种高维性通过特征选择和特征提取方法得以解决，并提高了文本分类的性能。特征选择和特征提取技术从文本文档中删除了不相关的特征，并降低了特征空间的维数。本文介绍了各种特征选择和特征提取方法。本文还讨论了用于对文档进行分类的各种分类器。

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来源
《Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking》|2016年|2264-2268|共5页
会议地点 Chennai(IN)
作者
Foram P. Shah; Vibha Patel;
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作者单位

Department of Computer Engineering, CGPIT, Uka Tarsadia University, Bardoli, Surat, India;

Department of Computer Engineering, CGPIT, Uka Tarsadia University, Bardoli, Surat, India;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Principal component analysis; Large scale integration; Feature extraction; Frequency measurement; Indexing; Support vector machine classification;

机译：主成分分析;大规模集成;特征提取;频率测量;索引;支持向量机分类;

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4. A Review on Feature Selection and Feature Extraction for Text Classification [C] . Foram P. Shah, Vibha Patel International Conference on Wireless Communications, Signal Processing and Networking . 2016

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A review on feature selection and feature extraction for text classification

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