NDLI: Double K-Folds in SVM

Content Provider	IEEE Xplore Digital Library
Author	Fengming Chang Hsing-Chung Chen Hsiang-Chuan Liu
Copyright Year	2015
Description	Author affiliation: Dept. of Inf. Manage., Nat. Taitung Junior Coll., Taitung, Taiwan (Fengming Chang) \|\| Dept. of Comput. Sci. & Inf. Eng., Asia Univ., Taichung, Taiwan (Hsing-Chung Chen) \|\| Grad. Inst. of Bioinf., Asia Univ., Taichung, Taiwan (Hsiang-Chuan Liu)
Abstract	In the K-folds cross validation process for Support Vector Machine (SVM) arguments determination, checking data has taken part in the seeking of the arguments values, hence the prediction accuracy tested by checking data is not independent. To avoid this condition, double K-folds are proposed in this study. (K-1)-folds are used for data training for the best SVM arguments determination, and the Kth fold is reserved for data checking. There are 10 data sets are used to check the proposed double K-folds methods. Without doubt, the learning accuracy in K-folds is better than that in double K-folds. However, it indicated that the results of double K-folds are almost as good as those of traditional K-folds.
Starting Page	384
Ending Page	387
File Size	295493
Page Count	4
File Format	PDF
e-ISBN	9781479988730
DOI	10.1109/IMIS.2015.59
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-08
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Training data Double K-folds Data models Cross validation SVM Expert systems Testing
Content Type	Text
Resource Type	Article

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