Induction of Decision Trees-英文文献.pdf

Machine Learning 1: 81-106, 1986 © 1986KluwerAcademicPublishers, Boston - Manufactured in The Netherlands Induction of Decision Trees J.R. QUINLAN (munnari! nswitgould.oz!quinlan@ seismo.css.gov) Centrefor Advanced Computing Sciences, New South Wales Institute o f Technology, Sydney 2007, Australia (Received August 1, 1985) Key words: classification, induction, decision trees, information theory, knowledge acquisition, expert systems Abstract. The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions. 1. Introduction Since artificial intelligence first achieved recognition as a discipline in the mid 1950s, machine learning has been a central research area. Two reasons can be given for this prominence. The ability to learn is a hallmark of intelligent behavior, so any attempt to understand intelligence as a phenomenon must include an understanding of learn- ing. More concretely, learning provides a potential methodology for building high- performance systems. Research on learning is made up of diverse subfields.