Application of Artificial Neural Network and Information Gain in Building Case-Based Reasoning for Telemarketing Prediction

Abstract

Traditionally, case-based reasoning (CBR) has been used as advanced technique for representing expert knowledge and reasoning. However, for stochastic business data such as customers’ behavior and users’ preferences, the knowledge cannot be extracted directly from data to build the cases in reasoning in making prediction. Artificial Neural Network that is known to be able to build model for predicting unprecedented business data is used together with Shannon Entropy and Information Gain (IG) to identify the key features. 8 attributes have been identified as key features from the 17 attributes which are based on the telemarketing data. These attributes are used to select the key features in building CBR. The weightage for the key features in the cases is obtained from the IG values. The mechanism of creating the cases based on the input from the ANN is discussed and the integration process between ANN and CBR is given. The process of integrating the ANN and CBR shows that both techniques complement each other in building a model in predicting a customer who would subscribe one of the promoted new banking service called “term deposit”.