Using Logic Minimization to Support Learning from Examples

This report demonstrates the e ectiveness of logic minimization in learning from examples. Initially the paper reviews logic minimization and relates it with learning. To support logic minimization we present a system (called LML), the core of which derives from the implementation of the Espresso-II algorithm (Brayton et al., 1984). Espresso-II is popular in VLSI synthesis and design. We show that logic minimization extends the general logic diagram approach as used to support conceptual clustering (Michalski & Stepp, 1983) and diagrammatic visualization of concepts (Wnek & Michalski, 1994) in learning from examples. We test our approach using two toy domains and ten real world domains. We discuss search space taken into account by logic minimization. Furthermore, we compare performance of LML with C4.5, AQ15, NewId and CN2 using classi cation accuracy, rule quality, and draw curves with respect to the number of training examples required for learning. We conclude our work by linking LML with similar machine learning systems. Abstract FORTH-ICS / TR-144 November 1995