Title :
A New Knn Categorization Algorithm for Harmful Information Filtering
Author :
Juan Du ; Zhi an Yi
Author_Institution :
Software Coll., Northeast Pet. Univ., Daqing, China
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
DOI :
10.1109/ISCID.2012.128
