A Hyper-Heuristic for Descriptive Rule Induction

Rule induction from examples is a machine learning technique that finds rules of the form condition → class, where condition and class are logic expressions of the form variable1 = value1 ∧ variable2 = value2 ∧… ∧ variablek = valuek. There are in general three approaches to rule induction: exhaustive search, divide-and-conquer, and separate-and-conquer (or its extension as weighted covering). Among them, the third approach, according to different rule search heuristics, can avoid the problem of producing many redundant rules (limitation of the first approach) or nonoverlapping rules (limitation of the second approach). In this paper, we propose a hyper-heuristic to construct rule search heuristics for weighted covering algorithms that allows producing rules of desired generality. The hyper-heuristic is based on a PN-space, a new ROC-like tool for analysis, evaluation and visualization of rules. Well-known rule search heuristics such as entropy, Laplacian, weight relative acc...

• Type: Journal
• Year: 2007
• Where: JDWM
• Authors: Tho Hoan Pham, Tu Bao Ho
• Keywords: JDWM 2007, Non-linear Rule Search, Rule Induction, Rule Search Heuristics