Beyond Concise and Colorful: Learning Intelligible Rules

Michael J. Pazzani, Subramani Mani, W. Rodman Shankle

A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful categories. However, research in data mining has not paid attention to the cognitive factors that make learned categories intelligible to human users. We show that one factor that influences the intelligibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind.

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