Description
Title: MULTI-CLASSIFIER WRAPPER FEATURE-SELECTION MODEL THAT IS EFFICIENT. APPLICATION FOR CREDIT SCORING DIMENSION REDUCTION
Abstract: Finding the features that are most important for a credit-scoring application is a difficult task. For a credit-scoring model to perform better, it is necessary to eliminate redundant and unwanted features. The wrapper approach is typically used to find the most pertinent features in credit-scoring applications. This method, however, has problems with subset generation and only uses one classifier as an evaluation function. The issue with this is that each classifier may produce unique results that can be interpreted in various ways. As a result, we in this study propose an ensemble wrapper multi-classifier combination feature selection model. By reducing the search space using a unique heuristic in the first stage, we tackle the issue of subset generation. Then, using two-classifier arrangements, a multi-classifier wrapper evaluation is applied in order to choose a set of relevant feature sets that have been mutually agreed upon. The proposed approach performed well when compared to the output of individual classifiers in four credit dataset evaluations.
Keywords: multi-classifier, heuristic, dimensionality reduction, credit scoring
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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