WSEAS Transactions on Business and Economics
Print ISSN: 1109-9526, E-ISSN: 2224-2899
Volume 10, 2013
Assessment of Customer Credit with Efficient Multivariate Classifier
Authors: ,
Abstract: The assessment of customer credit is important for financial institutions. Many techniques are developed for customer credit classification. Traditional methods suffer from the problems of inaccurate prediction and/or inefficient data analysis. In this paper, we adopt the entropy-based evaluation method and PCA-based classifier to improve these two problems. A new feature evaluation criterion collaborated with a novel feature selection are proposed to identify the critical factors of classification. By means of multivariate analysis, not only a reduced subset of relevant features is achieved but also the efficient classifier is generated. This paper aims at assessing customer credit with effectiveness and efficiency. From the experimental results of German credit case, it shows that our propsed method can simultanously improve accuracies in the classes of high-credit and bad-cedit when competing with the traditional C4.5 scheme.
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Keywords: Customer credit assessment, feature selection, multi-class classification, PCA, multivariate, analysis, C4.5