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SVM-Based Multiclass Cost-Sensitive Learning and its Application(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2006年04期
Page:
79-82
Research Field:
Publishing date:

Info

Title:
SVM-Based Multiclass Cost-Sensitive Learning and its Application
Author(s):
CHENG Xueyun1;  2 ;  J I Ge n lin1 ;  L IN Xiao han1
1. School ofMathematics and Computer Science, Nanjing Normal University, Nanjing 210097, China;
2. School of Computer Science and Technology, Nantong University, Nantong 226007, China
Keywords:
cost-sensitive learning;  support vectormachine ( SVM) ;  intrusion detection;  false negative;  false posi-tive
PACS:
TP181
DOI:
-
Abstract:
The standard classifier is usually based on minimizing the error rate, but in intrusion detection and some p ractical p roblems, different errors have different costs. Three kinds of support vector machine ( SVM) learning methods based on minimizing the totalmisclassification cost are p roposed, which introduce the cost-sensitive mecha2 nism into the p robabilistic outputs of SVM. The results show thatwe can trade off among false negative , false positive and error rate of rare class by changing cost matrix, which can minimize false negatives and error rate of rare class while constraining false positives at a low level so as to minimize the totalmisclassification cost.

References:

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Memo

Memo:
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Last Update: 2013-04-29