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Yawen Chen

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2 papers

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AAAI Conference 2018 Short Paper

Selecting Proper Multi-Class SVM Training Methods

Yawen Chen
Zeyi Wen
Jian Chen
Jin Huang

Support Vector Machines (SVMs) are excellent candidate solutions to solving multi-class problems, and multi-class SVMs can be trained by several different methods. Different training methods commonly produce SVMs with different effectiveness, and no multi-class SVM training method always outperforms other multi-class SVM training methods on all problems. This raises difﬁculty for practitioners to choose the best training method for a given problem. In this work, we propose a Multi-class Method Selection (MMS) approach to help users select the most appropriate method among one-versus-one (OVO), one-versus-all (OVA) and structural SVMs (SSVMs) for a given problem. Our key idea is to select the training method based on the distribution of training data and the similarity between different classes. Using the distribution and class similarity, we estimate the unclassiﬁable rate of each multi-class SVM training method, and select the training method with the minimum unclassiﬁable rate. Our initial ﬁndings show: (i) SSVMs with linear kernel perform worse than OVO and OVA; (ii) MMS often produces SVM classiﬁers that can conﬁdently classify unseen instances.

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AAAI Conference 2017 Conference Paper

Improving Efficiency of SVM k -Fold Cross-Validation by Alpha Seeding

Zeyi Wen
Bin Li
Ramamohanarao Kotagiri
Jian Chen
Yawen Chen
Rui Zhang

The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters. It is known that the SVM k-fold cross-validation is expensive, since it requires training k SVMs. However, little work has explored reusing the hth SVM for training the (h + 1)th SVM for improving the efﬁciency of k-fold cross-validation. In this paper, we propose three algorithms that reuse the hth SVM for improving the efﬁciency of training the (h + 1)th SVM. Our key idea is to efﬁciently identify the support vectors and to accurately estimate their associated weights (also called alpha values) of the next SVM by using the previous SVM. Our experimental results show that our algorithms are several times faster than the k-fold cross-validation which does not make use of the previously trained SVM. Moreover, our algorithms produce the same results (hence same accuracy) as the k-fold cross-validation which does not make use of the previously trained SVM.

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