A New Knn Categorization Algorithm for Harmful Information Filtering

Author

Juan Du ; Zhi an Yi

Author_Institution

Software Coll., Northeast Pet. Univ., Daqing, China

Volume

1

fYear

2012

fDate

28-29 Oct. 2012

Firstpage

489

Lastpage

492

Abstract

The prediction result of classifier is biased towards the class with more samples, when the harmful text information is filtered. This is because that the samples that including the harmful information were difficult to gain. Construct virtual samples is an effective means to solve the problem of pattern recognition in the small sample, using the up-sampling method to construct virtual samples in the data layer, the traditional KNN algorithm has been improved: a small sample set is divided into clusters by using the K-means clustering, the virtual samples are generated and verified the validity in the cluster. The experimental results show that this method can construct the virtual samples which are similar to the real sample characteristics, and improved the classification effect of KNN algorithm.

Keywords

information filtering; learning (artificial intelligence); pattern classification; pattern clustering; sampling methods; text analysis; KNN algorithm; KNN categorization algorithm; classification effect; data layer; harmful information filtering; harmful text information; k-means clustering; pattern recognition; up-sampling method; Classification algorithms; Clustering algorithms; Genetic algorithms; Genetics; Information filtering; Support vector machine classification; Training; Harmful information filtering; Network information security; Small sample pattern recognition; Virtual sample;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-2646-9

Type

conf

DOI

10.1109/ISCID.2012.128

Filename

6407028