Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach

摘要

Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (>104 examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs.