NDLI: Using Rough Reducts Based SVM Ensemble for SAR of the Ethofenprox Analogous of Pesticide

Content Provider	IEEE Xplore Digital Library
Author	Yue Liu Zaixia Teng Yafeng Yin Guo-Zheng Li
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai (Yue Liu; Zaixia Teng; Yafeng Yin; Guo-Zheng Li)
Abstract	Neural networks ensemble is a promising tool in the field of structure-activity relationship (SAR). Based on support vector machine (SVM), a new method called RRSE (rough reducts based SVM ensemble) is employed to discriminate between high and low activities of ethofenprox analogous based on the molecular descriptors. By using RRSE, individual SVMs of ensemble model are constructed by projection of training dataset on sufficient and necessary attribute sets (reducts). Finally, the results from all individuals are combined by majority voting to finalize the ensemble results which predict activities of ethofenprox analogous with accuracy of 93.5%. Experimental results indicate that performance of RRSE is better than those of SVM bagging, optimal reducts based SVM and single SVM. Therefore, RRSE could be a promising and useful tool in SAR research.
Starting Page	25
Ending Page	30
File Size	328035
Page Count	6
File Format	PDF
ISBN	9780769534305
DOI	10.1109/IMSCCS.2008.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Crops Humans Rough set Support vector machines Voting Neural networks Neural networks ensemble Support vector machine classification Soil Computer networks structure-activity relationship Bagging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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