SMART+: A Multi-Strategy Learning Tool

Abstract

Inducing concept descriptions in First Order Logic is inherently a complex task. There are two main reasons: on one hand, the task is usually formulated as a search problem inside a very large space of logical descriptions which needs strong heuristics to be kept to manageable size. On the other hand, most developed algorithms are unable to handle numerical features, typically occurring in realworld data. In this paper, we describe the learning system SMART+, that embeds sophisticated knowledge-based heuristics to control the search process and is able to deal with numerical features. SMART+ can use different learning strategies, such as inductive, deductive and abductive ones, and exploits both backgruond knowledge and statistical evaluation criteria. Furthermore, it can use simple Genetic Algorithms to refine predicate semantics and this aspect will be described in detail. Finally , an evaluation of SMART+ performances is made on a complex task.