NDLI: Two-stage SVMs for Solving Multi-class Problems

Content Provider	IEEE Xplore Digital Library
Author	Li Qi Yushu Liu
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol. (Li Qi; Yushu Liu)
Abstract	The conventional pairwise classification shows superior performances for those classifiable samples, but unclassifiable regions exist. DDAG based SVMs can resolve unclassified regions, but it only employ one decision function at each step, and never considers all decision function together, which may hurt its classification performance. In this paper we propose two-stage SVMs to combine their merits together, which resolve unclassifiable regions and keep the classification results same as conventional pairwise classification for the data in classifiable regions. To classify the data in unclassifiable regions, optimal DDAG based on heuristic information is proposed. Two heuristic measures are designed: static heuristic information (SHI) and dynamic heuristic information (DHL). Based on these two heuristic measures, two strategies of constructing DDAG are proposed. Experimental results based on three benchmark datasets demonstrate the superiority of our two-stage SVMs over traditional pairwise classification and DDAG based SVMs
Starting Page	78
Ending Page	83
File Size	6550697
Page Count	6
File Format	PDF
ISBN	1424406048
DOI	10.1109/ICCIAS.2006.294094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Tree data structures Learning systems Voting Support vector machine classification Decision trees Risk management Kernel Classification tree analysis
Content Type	Text
Resource Type	Article

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