Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methods

摘要

Some recent work in the area of learning structural descriptions from examples is reviewed in light of the need in many diverse disciplines for programs which can perform conceptual data analysis, i.e., can describe complex data in terms of logical, functional, and causal relationships. Traditional data analysis techniques are not adequate for discovering such relationships. Primary attention is given to methods of learning the simplest form of generalization, namely, the maximally specific conjunctive generalizations (MSC-generalizations) which completely characterize a single set of structural examples. Various important aspects of structural learning in general are examined and criteria for evaluating learning methods are presented. The criteria include the adequacy of the representation language, generalization rules used, computational efficiency, and flexibility and extensibility. Selected learning methods, developed by Buchanan et al. [2–4, 32], Hayes-Roth [8–11], Vere [34–37], Winston [38, 39], and the authors, are analyzed according to these criteria. Finally some goals are suggested for future research.