NDLI: Granular support vector machines with data cleaning for fast and accurate biomedical binary classification

Content Provider	IEEE Xplore Digital Library
Author	Yuchun Tang Yan-Qing Zhang
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA (Yuchun Tang; Yan-Qing Zhang)
Abstract	This paper presents a new algorithm to model fast and accurate granular support vector machines (GSVMs) for biomedical binary classification problems. The algorithm, named GSVM-DC, splits the original training dataset into several highly overlapping granules, from which local support vectors (LSVs) are extracted. Then cross validation heuristic are adopted to optimize the SVM parameters. Finally, GSVM-DC combines these LSVs into a new compressed training dataset, on which a SVM with the optimized parameters is modeled for classification. The proposed GSVM-DC algorithm is fast due to the usually small size of LSVs. It is also expected to be accurate due to reservation of important data, which are essential for classification and elimination of large quantities of redundant data, which may confuse a classifier to find optimal decision boundary. The simulation results on three biomedical datasets prove that the expectation is reasonable. In general, GSVM provides an interesting new mechanism to address complex classification problems effectively and efficiently in the biomedical domain.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	262
Ending Page	265
File Size	1838753
Page Count	4
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Biomedical informatics Biomedical computing Data processing Cleaning Data mining Biomedical Informatics Granular Computing Diseases Support vector machines Learning systems Granular Support Vector Machines Support vector machine classification Data Cleaning
Content Type	Text
Resource Type	Article

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