Opportunistic Constructive Induction: Using Fragments of Domain Knowledge to Guide Construction

One subfield of machine learning is the induction of a representation of a concept from positive and negative examples of the concept. Given a set of training examples, the goal of the inductive system is to create a description capable of classifying the training examples, yet general enough to accurately predict the classification of unseen examples. Often the original attributes describing the instances are inadequate to capture important regularities in the concept. New descriptors, constructed through the application of operators to the original attributes, can provide the proper vocabulary to create concise concept representations at the right level of generalization to be highly predictive. Constructive induction is the process of generating and applying new descriptors during inductive learning. The large number of possible constructive operators and combinations of attributes defines an enormous search space for the inductive process. Knowledge about the concept or problem domain can be used to guide the construction of new descriptors. This thesis lays the foundation of opportunistic constructive induction in the context of decision-tree assembly, providing a framework for dynamically applying fragments of knowledge to produce potentially useful descriptors or hypotheses. Personally Identifiable Information (PII) was redacted.