Multistrategy Constructive Induction: A Theory and Methodology for Task-Oriented Improvement of Knowledge Representation Spaces for Learning

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This research concerns a fundamental problem of machine learning, which is how to learn when the originally given representation space is of low quality. Most machine learning methods search for desirable knowledge in the same representation space in which training examples are presented (that is, use the same attributes or terms as those employed in the training data). If the original representation space is inadequate, that is, the given attributes or descriptive terms are insufficiently relevant to the present learning task, such methods do not perform well. This project investigates several novel ideas and methods for learning in such situations. In particular, we are investigating a constructive induction approach to the problem, in which a learning system performs a double intertwined search - one for an improved knowledge representation and one for the "best" hypothesis in that space. In the multistrategy constructive induction methodology, a combination of strategies is employed for transforming the initial representation space into the new task-oriented space. the space transformation operators are divided into "constructors" that expand the space by inventing new attributes and "destructors" that reduce the space by removing or abstracting the current attributes, guided by "meta rules" that are initially provided, but incrementally improved by a secondary learning process.