Stochastic Propositionalization of Non-determinate Background Knowledge

It is a well-known fact that propositional learning algorithms require \good" features to perform well in practice. So a major step in data engineering for inductive learning is the construction of good features by domain experts. These features often represent properties of structured objects, where a property typically is the occurrence of a certain substruc-ture having certain properties. To partly automate the process of \feature engineering", we devised an algorithm that searches for features which are deened by such substructures. The algorithm stochastically conducts a top-down search for rst-order clauses, where each clause represents a binary feature. It diiers from existing algorithms in that its search is not class-blind, and that it is capable of considering clauses (\context") of almost arbitrary length (size). Preliminary experiments are favorable, and support the view that this approach is promising.