Inductive Learning in a Mixed Paradigm Setting

David B. Skalak, Edwina E. Rissland

In a precedent-bused domain one appeals to previous cases to support a solution, decision, explanation, or an argument. Experts typically use care in choosing cases in precedent-based domains, and apply such criteria as case relevance, prototypicality, and importance. In domains where both cases and rules are used, experts use an additional case selection criterion: the generalizations that a particular group of cases support. Domain experts use their knowledge of cases to forge the rules learned from those cases. In this paper, we explore inductive learning in a "mixed paradigm" setting, where both rule-based and case-based reasoning methods are used. In particular, we consider how the techniques of case-based reasoning in an adversarial, precedent-based domain can be used to aid a decision-tree based classification algorithm for (1) training set selection, (2) branching feature choice, and (3) induction policy preference and deliberate exploitation of inductive bias. We focus on how precedent-based argumentation may inform the selection of training examples used to build classification trees. The resulting decision trees may then be reexpressed as rules and incorporated into the mixed paradigm system. We discuss the heuristic control problems involved in incorporating an inductive learner into CABARET, a mixed paradigm reasoner. Finally, we present an empirical study in a legal domain of the classification trees generated by various training sets constructed by a case-based reasoning module.

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