Efficient multi-classifier wrapper feature-selection model. Application for dimension reduction in credit scoring

• Autorzy: Bouaguel Waad

Identyfikatory

DOI

10.7494/csci.2022.23.1.4120

• Języki publikacji: EN

Abstrakty

EN

The task of identifying the most relevant features for a credit-scoring application is a challenging task. Reducing the number of redundant and unwanted features is an inevitable task for improving the performance of a credit-scoring model. The wrapper approach is usually used in credit-scoring applications to identify the most relevant features. However, this approach suffers from the issue of subset generation and the use of a single classifier as an evaluation function. The problem here is that each classifier may give different results that can be interpreted differently. Hence, we propose an ensemble wrapper featureselection model in this study that is based on a multi-classifier combination. In the first stage, we address the problem of subset generation by minimizing the search space through a customized heuristic. Then, a multi-classifier wrapper evaluation is applied using two-classifier-arrangement approaches in order to select a set of mutually approved sets of relevant features. The proposed method was evaluated on four credit datasets and has shown good performance as compared to individual classifier results.

Słowa kluczowe

EN

multi-classifier; heuristic; dimensionality reduction; credit scoring

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (1)
• Strony: 133--155
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Bouaguel Waad

• University of Jeddah, College of Business, Jeddah, Saudi Arabia
• University of Tunis, LARODEC, ISG, Tunisia

• https://orcid.org/0000-0003-2171-0370

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).