Title
Feature weighted confidence to incorporate prior knowledge into support vector machines for classification
Abstract
This paper proposes an approach called feature weighted confidence with support vector machine (FWC–SVM) to incorporate prior knowledge into SVM with sample confidence. First, we use prior features to express prior knowledge. Second, FWC–SVM is biased to assign larger weights for prior weights in the slope vector \(\omega \) than weights corresponding to non-prior features. Third, FWC–SVM employs an adaptive paradigm to update sample confidence and feature weights iteratively. We conduct extensive experiments to compare FWC–SVM with the state-of-the-art methods including standard SVM, WSVM, and WMSVM on an English dataset as Reuters-21578 text collection and a Chinese dataset as TanCorpV1.0 text collection. Experimental results demonstrate that in case of non-noisy data, FWC–SVM outperforms other methods when the retaining level is not larger than 0.8. In case of noisy data, FWC–SVM can produce better performance than WSVM on Reuters-21578 dataset when the retaining level is larger than 0.4 and on TanCorpV1.0 dataset when the retaining level is larger than 0.5. We also discuss the strength and weakness of the proposed FWC–SVM approach.
Year
DOI
Venue
2019
10.1007/s10115-018-1165-2
Knowledge and Information Systems
Keywords
Field
DocType
Feature weighted confidence,Prior knowledge,Support vector machine,Classification
Noisy data,Computer science,Support vector machine,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
58.0
2.0
0219-3116
Citations 
PageRank 
References 
2
0.36
23
Authors
4
Name
Order
Citations
PageRank
Wen Zhang192.16
Lean Yu21777179.93
Taketoshi Yoshida330616.80
Qing Wang434576.64