Summary
This chapter is concentrated with the performance characterization of a case-based reasoning (CBR) system. Based on the match score and nonmatch score computed from the cases in the case library, we develop a statistical model for prediction. We estimate the size of a subset of cases, called gallery size, that can generate the optimal error estimate and its confidence on a large population (relative to the size of the gallery). The statistical model is based on a generalized two-dimensional prediction model that combines a hypergeometric probability distribution model with a binomial model explicitly and considers the data distortion problem in large populations. Learning is incorporated in the prediction process in order to find the optimal small gallery size and to improve the prediction performance. During the prediction, the expectation-maximization (EM) algorithm is used to learn the match score and the nonmatch score distributions that are represented as mixture of Gaussians. By learning, the optimal size of small gallery is determined and at the same time the upper bound and the lower bound for the prediction on large populations are obtained. Results are shown using a real-world database with the increasing size of the case library.
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Bhanu, B., Wang, R. (2008). Learning a Statistical Model for Performance Prediction in Case-Based Reasoning. In: Perner, P. (eds) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73180-1_7
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DOI: https://doi.org/10.1007/978-3-540-73180-1_7
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