Abstract
Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
D. Wettschereck and D.W. Aha, “Weighting features,” in Proc. ICCBR-95, edited by A. Aamodt and M. Veloso, 1995, pp. 347–358.
J.D. Kelly and L. Davis, “A hybrid genetic algorithm for classi-fication,” in Proc. of the Twelfth International Joint Conference on Artificial Intelligence, Sydney, Australia, 1991, pp. 645–650.
I. Inza, P. Larranaga, and B. Sierra, FeatureWeighting for Nearest Neighbor by Estimation of Bayesian Networks Algorithms, University of the Basque Country, Technical Report, EHU-KZAAIK-3/00, 2000.
C. Shin, U.T. Yun, H.K. Kim, and S.C. Park, “A hybrid approach of neural network and memory based learning for data mining,” IEEE Trans. on Neural Networks, vol. 11, no. 3, pp. 637–646, 2000.
D.W. Aha and R.L. Goldstone, “Concept learning and flexible weighting,” in Proc. of the Fourteenth Annual Conference of the Cognitive Science Society, Bloomington, IN, 1992, pp. 534–539.
I.Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann Publishers, Inc.: San Francisco, CA, 1997.
F. Ricci and P. Avesani, Nearest Neighbor Classification with a Local AsymmetricallyWeighted Metric, IRST, Povo, Italy, Technical Report, 1996.
N. Howe and C. Cardie, “Examining Locally Varying Weights for Nearest Neighbor Algorithms,” Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning, edited by D. Leake and E. Plaza, Lecture Notes in Artificial Intelligence, Springer, 1997, pp. 455–466.
T. Hastie and R. Tibshirani, “Discriminant Adaptive Nearest Neighbor Classification,” Neural Computation, vol. 7, no. 1, pp. 72–85, 1995.
J.H. Friedman, Flexible Metric Nearest Neighbor Classification, Dept. of Statistics, Stanford University: Stanford, CA, Technical Report, 1994.
C. Domeniconi, J. Peng, and D. Gunopulos, Locally Adaptive Metric Nearest Neighbor Classification, Univ. of California, Riverside, CA, Technical Report, 2000.
S.K. Pal, T.S. Dillon, and D.S. Yeung, Soft Computing in Case Based Reasoning, Springer: London, 2001.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Park, J.H., Im, K.H., Shin, CK. et al. MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network. Applied Intelligence 21, 265–276 (2004). https://doi.org/10.1023/B:APIN.0000043559.83167.3d
Issue Date:
DOI: https://doi.org/10.1023/B:APIN.0000043559.83167.3d