Learning Decision Rules by Randomized Iterative Local Search
Michael Chisholm, Prasad V. Tadepalli · 2002
Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning systems for this problem employ some variation of a greedy separate-and-conquer algorithm, which makes the rules order-dependent, and hence difficult to understand. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a randomized iterative local search. We compare its performance to C4.5, RIPPER, CN2, G-NET, Smog, and BruteDL, and show that it compares favorably in accuracy and simplicity of hypotheses in a number of domains.