Learning Adaptations for Case-Based Classification: A Neural Network Approach

Case-based Reasoning (CBR) solves a new problem by retrieving a stored case for a similar problem and adapting its solution to fit. Acquiring the required case adaptation knowledge is a classic problem. A popular method for addressing it is the case difference heuristic (CDH) approach, which learns adaptations from pairs of cases based on their problem differences and solution differences. The CDH approach was originally used to generate adaptation rules, but recent CBR research on case-based regression has investigated replacing learning rules with learning CDH-based network models for adaptation. This paper presents and evaluates a neural network-based CDH approach for learning adaptation models for classification, C-NN-CDH. It examines three variants, (1) training a single neural network on problem-solution differences, (2) segmenting adaptation knowledge by the classes of source cases, with a separate neural network to generate adaptations for each class, and (3) adapting from an ensemble of source cases and taking the majority vote. Experimental results demonstrate improved performance compared to previous research on statistical methods for computing CDH differences for classification. Additional results support that C-NN-CDH achieves classification performance comparable to that of multiple classic classification approaches.