A Fuzzy Knowledge Based System for Financial Credit Classification
Praveen Kumar Dwivedi1, Surya Prakash Tripathi2
1Praveen Kumar Dwivedi, 1. Software Technology Parks of India, Ministry of Electronics and IT, Govt. of India, Lucknow, India. 2. Dr. APJ Abdul Kalam Technical University, Lucknow, India.
2Surya Prakash Tripathi, Department of Computer Science and Engineering, Institute of Engineering and Technology, Lucknow, India.
Manuscript received on 17 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 7664-7673 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6234098319/2019©BEIESP | DOI: 10.35940/ijrte.C6234.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fuzzy knowledge-based systems are successfully applied in several areas to classify and modelling the knowledge base using fuzzy If then rules. In recent era, taking the loan from banking system is highly practiced and the finding the eligible person to grant the credit is challenging task. In this context, this article designed a fuzzy knowledge base system and defined eight rules for credit allocation system and implemented on two different dataset German credit allocation system and Australian credit allocation system. These data are downloaded from well-known machine learning repository UCI. To classify the credit allocation data, fuzzy decision tree and Wang and Mendel model has been used. To estimate the performance of the proposed method for credit allocation system the accuracy and the interpretability is used. The experimental analysis highlight that the Wand and Mendel model gives higher accuracy i.e. 99.9% and the interpretability of the proposed model is very less or negligible.
Index Terms: Fuzzy Classifier; Fuzzy Rules; Knowledge Base; Financial Credit Classification, etc.
Scope of the Article: Classification