A Categorization Algorithm for Harmful Text Information Filtering

Author

Juan Du ; Zhi an Yi

Author_Institution

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

fYear

2012

fDate

2-4 Nov. 2012

Firstpage

31

Lastpage

34

Abstract

Harmful text information filtering is a typical pattern recognition problem of small sample, the prediction result of classifier was biased towards the class with more samples, because of 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 expand the small sample collection in order to effectively identify the harmful text information.

Keywords

information filtering; pattern classification; pattern clustering; sampling methods; text analysis; K-means clustering; categorization algorithm; classifier prediction result; data layer; harmful text information filtering; improved KNN algorithm; pattern recognition problem; real sample characteristics; up-sampling method; virtual sample generation; 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

Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-3093-0

Type

conf

DOI

10.1109/MINES.2012.13

Filename

6405624