[1]戚后林,顾磊. 概率潜在语义分析的KNN文本分类算法[J].计算机技术与发展,2017,27(07):57-61.
　QI Hou-lin,GU Lei. KNN Text Classification Algorithm with Probabilistic Latent Semantic Analysis[J].,2017,27(07):57-61.
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 概率潜在语义分析的KNN文本分类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年07期
页码:
57-61
栏目:
智能、算法、系统工程
出版日期:
2017-07-10

文章信息/Info

Title:
 KNN Text Classification Algorithm with Probabilistic Latent Semantic Analysis
文章编号:
1673-629X（2017)07-0057-05
作者:
 戚后林; 顾磊
 南京邮电大学 计算机学院
Author(s):
 QI Hou-lin; GU Lei
关键词:
 ; 文本分类; KNN算法; 文本表示模型; 语义分类; 概率潜在主题模型
Keywords:
 ; text classification; KNN; text presentation model; semantic classification; probability latent semantic analysis
分类号:
TP301.6
文献标志码:
A
摘要:
 传统的KNN文本算法在计算文本之间的相似度时,只是做简单的概念匹配,没有考虑到训练集与测试集文本中词项携带的语义信息,因此在利用KNN分类器进行文本分类过程中有可能导致语义丢失,分类结果不准确.针对这种情况,提出了一种基于概率潜在主题模型的KNN文本分类算法.该算法预先使用概率主题模型对训练集文本进行文本-主题、主题-词项建模,将文本携带的语义信息映射到主题上的低维空间,把文本相似度用文本-主题、主题-词项的概率分布表示,对低维文本的语义信息利用KNN算法进行文本分类.实验结果表明,在训练较大的训练数据集和待分类数据集上,所提算法能够利用KNN分类器进行文本的语义分类,且能提高KNN分类的准确率和召回率以及F1值.
Abstract:
 Traditional KNN Text Classification (TC) algorithm just implements a simple concept matching during calculation of the similarity between texts without taking the semantic information of the text in training and test set into account.Thus it is possible to lose semantic meaning in the process of text classification with KNN classifier as well as inaccurate categorization results.Against this problem,a KNN text classification algorithm based on probabilistic latent topic model has been proposed,which establishes probabilistic topic models of text-theme,theme-lexical item for training set texts beforehand to map the semantic information to low dimensional space of theme and dictates text similarity with probability distributions of text-theme and theme-lexical.The semantic information of low dimensional text can be classified with the proposed KNN algorithm.The experimental results show that in training of large training dataset and unclassified dataset,the proposed algorithm can conduct semantic classification of text with KNN classifier and enhance the accuracy and recall rate as well as F1 measure in KNN classification.

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更新日期/Last Update: 2017-08-22