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Abstract. Case-based regression often relies on simple case adaptation methods. This paper investigates new approaches to enriching the adaptation capabilities of case-based regression systems, based on the use of ensembles of adaptation rules generated from the case base. The paper explores both local and global methods for generating adaptation rules from the case base, and presents methods for ranking the generated rules and combining the resulting ensemble of adaptation rules to generate new solutions. It tests these methods in five standard domains, evaluating their performance compared to four baseline methods, standard k-NN, linear regression, locally weighted linear regression, and an ensemble of k-NN predictors with different feature subsets. The results demonstrate that the proposed method generally outperforms the baselines and that the accuracy of adaptation based on locally-generated rules is highly competitive with that of global rule-generation methods with much greater computational cost.

The ability of case-based reasoning systems to solve novel problems depends on their capability to adapt past solutions to new circumstances. However, acquiring the knowledge required for case adaptation is a classic challenge for CBR. This motivates the use of machine learning to generate adaptation knowledge. Much adaptation learning research has studied the case difference heuristic (CDH) approach, which generates adaptation rules from pairs of cases in the case base by ascribing observed differences in case solutions to the differences in the problems they solve, to generate rules for adapting similar problem differences. Extensive research has successfully applied the CDH approach to adaptation rule learning for case-based regression (numerical prediction) tasks. However, classification tasks have been outside of its scope. The work presented in this paper addresses that gap by extending CDH-based learning of adaptation rules to apply to cases with categorical features and solutions. It presents the generalized case value heuristic to assess case and solution differences and applies it in an ensemble-based casebased classification method, ensembles of adaptations for classification (EAC), built on the authors' previous work on ensembles of adaptations for regression (EAR). Experimental results support the effectiveness of EAC.

Domain specific ontologies can be used to improve both precision and recall of information retrieval systems. One approach in this regard is using query expansion techniques and the other would be introducing a semantic similarity measure for concepts in ontology. Although each approach has its own benefits and drawbacks, query expansion techniques are preferred when the corpus volume is so huge that examining concept pairs between query and documents is not reasonable. In this paper a semantic query expansion algorithm for medical information retrieval is introduced. Proposed approach consists of identifying MeSH (Medical Subject Headings) concepts in user's query and applying expansion algorithm to them. Expansion algorithm is based on the location of concepts in MeSH hierarchy, number of synonyms of each concept and number of terms the concept is made of. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.

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