Mathematical Analysis on Weight Vectors in Text Classification

被引：1

作者：

Song, Fengxi ^{[1
]}

Chen, Qinglong ^{[1
]}

Guo, Zhongwei ^{[1
]}

Gao, Xiumei ^{[2
]}

机构：

[1] New Star Res Inst Appl Tech Hefei City, Dept Automat & Simulat, Hefei 230031, Peoples R China

[2] Huaiyin Teachers Coll, Dept Comp Sci, Huaiyin 223001, Peoples R China

来源：

2012 THIRD GLOBAL CONGRESS ON INTELLIGENT SYSTEMS (GCIS 2012) | 2012年

基金：

美国国家科学基金会;

关键词：

evaluation; optimal classifier; text classification; weight vector;

D O I：

10.1109/GCIS.2012.14

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

By means of rigid mathematical deductions we prove that weight vectors cannot promote the performance of the optimal classifier, i.e. the Bayesian classifier in terms of the error, F-one score, or breakeven point. The conclusion is important in that people used to promote the performance of a classifier by trying various weight vectors in text classification.

引用

页码：148 / 151

页数：4

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