Dataset Information

Reinforced Angle-based Multicategory Support Vector Machines.

ABSTRACT: The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of k classification functions for a k-class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with k - 1 functions directly. We prove that RAMSVMs can enjoy Fisher consistency. Moreover, we show that the RAMSVM can be implemented using the very efficient coordinate descent algorithm on its dual problem. Numerical experiments demonstrate that our method is highly competitive in terms of computational speed, as well as classification prediction performance. Supplemental materials for the article are available online.

SUBMITTER: Zhang C

PROVIDER: S-EPMC5120762 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Reinforced Angle-based Multicategory Support Vector Machines.

Zhang Chong C Liu Yufeng Y Wang Junhui J Zhu Hongtu H

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20160805 3

The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of <i>k</i> classification functions for a <i>k</i>-class problem, and the corresponding optimization problems are typically handled by existing quadratic p ...[more]

PMID: 27891045

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Project description:Human longevity is a complex phenotype that has a significant genetic predisposition. Like other biological processes, ageing process is governed through the regulation of signaling pathways and transcription factors. The DNA damage theory of ageing suggests that ageing is a consequence of un-repaired DNA damage accumulation. Intensive research has been carried out to elucidate the role of DNA repair systems in the ageing process. Decision Trees and Naive Bayesian Algorithm are two data-mining based classification methods for systematically analyzing data about human DNA repair genes. In this paper we develop a linearly combined kernel with Support Vector Machine (SVM) to analyze the ageing related data. The popular supervised learning algorithm enables better discrimination between ageing-related and non-ageing-related DNA repair genes. The linear combination of linear kernel and polynomial kernel of degree 3 in conjunction with SVM allows better classification accuracy in DNA repair gene data set. Compared to Decision Trees and Naive Bayesian Algorithm, SVM with the proposed kernel can achieve 65% AUC (Area Under ROC Curve) values, in contrast to 51.1% and 52.1% respectively. More importantly, we obtain 5 significant ageingrelated genes selected through the training on the whole data set and they are PCNA, PARP, APEX1, MLH1 and XRCC6. Different from the two methods, we can identify another important gene PCNA in the pathways the two methods targeted, while they failed to. And two novel genes PARP, MLH1 are selected as well. The two genes might provide potential insights for biologists in ageing research. SVM is a powerful and robust classification algorithm that can yield higher predictive accuracies. The selection of proper kernel plays a more important role in fulfilling the classification task. The important genes identified not only can target critical pathways related to ageing but also detected genes that may reveal possible related ageing biomarkers.

Dataset Information

Reinforced Angle-based Multicategory Support Vector Machines.

Publications

Reinforced Angle-based Multicategory Support Vector Machines.

Similar Datasets

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