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Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique

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2 Author(s)
Manikandan, J. ; Dept. of Electron. & Commun. Eng. (ECE), Nat. Inst. of Technol., Trichy, Trichy, India ; Venkataramani, B.

Support vector machine (SVM) is the state-of-the-art classifier used in real world pattern recognition applications. One of the design objectives of SVM classifiers using non-linear kernels is reducing the number of support vectors without compromising the classification accuracy. To meet this objective, decision-tree approach and pruning techniques are proposed in the literature. In this study, optimum threshold (OT)-based pruning technique is applied to different decision-tree-based SVM classifiers and their performances are compared. In order to assess the performance, SVM-based isolated digit recognition system is implemented. The performances are evaluated by conducting various experiments using speaker-dependent and multispeaker-dependent TI46 database of isolated digits. Based on this study, it is found that the application of OT technique reduces the minimum time required for recognition by a factor of 1.54 and 1.31, respectively, for speaker-dependent and multispeaker-dependent cases. The proposed approach is also applicable for other SVM-based multiclass pattern recognition systems such as target recognition, fingerprint classification, character recognition and face recognition.

Published in:

Signal Processing, IET  (Volume:5 ,  Issue: 5 )

Date of Publication:

August 2011

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