Authors: Saroj Kr. Biswas; Barnana Baruah; Nidul Sinha; Biswajit Purkayastha
Addresses: National Institute of Technology Silchar, Silchar-788010, Assam, India ' National Institute of Technology Silchar, Silchar-788010, Assam, India ' National Institute of Technology Silchar, Silchar-788010, Assam, India ' National Institute of Technology Silchar, Silchar-788010, Assam, India
Abstract: Case-based reasoning (CBR) is an artificial intelligent approach to problem solving and learning, which understands and extracts knowledge from past cases. However, CBR faces the challenge of assigning weights to the features to measure similarity between cases effectively and correctly. Integration of inherent learning capability of artificial neural networks (ANNs) to help the CBR in attributing the correct and appropriate weights to the features is likely to improve the performance of the standard CBR approach. This paper integrates back propagation neural network (BPNN) into CBR in an innovative way to develop an efficient model for classification tasks. The implementation of integration of NN and CBR for classification tasks is done by building training and testing datasets and optimising NN architecture in terms of number of neurons in hidden layer. This paper investigates the integration of multi-layer BP neural network and CBR. The experimental results obtained with the proposed hybrid model are compared with that of standard CBR, CBR with value difference matrix (VDM) and one existing CBR with BPNN approaches. The superiority of the proposed hybrid CBR model is established to others. The performance of the proposed model is validated with four datasets.
Keywords: case-based reasoning; CBR; artificial neural networks; ANNs; network similarity measures; hybrid models; feature weighting; CBR classification models; value difference matrix; VDM.
International Journal of Services Technology and Management, 2015 Vol.21 No.4/5/6, pp.272 - 293
Received: 30 Dec 2014
Accepted: 24 May 2015
Published online: 29 Dec 2015 *