The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their\nbusiness. Good management of these risks is a key factor to increase profitability.Therefore, every bank needs to predict the credit\nrisks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This\npaper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The\nproposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we\nhave used immune memory to remember good B cells during the cloning process.We have designed two forms of memory: simple\nmemory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data\nto show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results\nobtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in\ndetecting credit risks.
Loading....