文本分类中权重向量的数学分析

2012 Third Global Congress on Intelligent Systems Pub Date : 2012-11-06 DOI:10.1109/GCIS.2012.14

Fengxi Song, Qinglong Chen, Zhongwei Guo, Xiumei Gao

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引用次数: 1

摘要

通过严格的数学推导，我们证明了权重向量不能提高最优分类器的性能，即贝叶斯分类器在误差、f - 1分数或盈亏平衡点方面的性能。这个结论很重要，因为人们过去常常通过尝试各种权重向量来提高分类器在文本分类中的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Mathematical Analysis on Weight Vectors in Text Classification

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.

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2012 Third Global Congress on Intelligent Systems

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