Cost Sensitive Relevance Vector Machine

Abstract

Abstract:

Relevance Vector Machine (RVM) is a sparse model proposed on the basis of sparse Bayesian framework, it has been widely studied and applied in the field of machine

learning in recent years because of its strong sparsity and generalization ability. However, like the traditional decision tree, neural network algorithm and support vector

machine, RVM does not have the expense of sensitivity, can not be directly used for costsensitive learning. To deal with the cost sensitive problem brought by

misclassification in supervised learning, costsensitive relevance vector classification(CSRVC) algorithm was proposed by integrating misclassification cost of each type

sample based on RVM. Experiments show that CSRVC has good sparsity and can effectively solve the problem of costsensitive classification.
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Key words: relevance vector machine(RVM), cost sensitive, CSRVC

CLC Number:

TP301.6

SU Lequn， FENG Aimin. Cost Sensitive Relevance Vector Machine[J]. Computer and Modernization.

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Cost Sensitive Relevance Vector Machine

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