Induction of Decision Trees

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]  DAVID G. KENDALL,et al.  Introduction to Mathematical Statistics , 1947, Nature.

[2]  Robert V. Hogg,et al.  Introduction to Mathematical Statistics. , 1966 .

[3]  W. R. Garner.  Concept Learning: An Information- Processing Problem , 1964 .

[4]  Philip J. Stone,et al.  Experiments in induction , 1966 .

[5]  A. L. Samuel,et al.  Some studies in machine learning using the game of checkers. II: recent progress , 1967 .

[6]  J. Ross Quinlan,et al.  A Task-Independent Experience-Gathering Scheme for a Problem Solver , 1969, IJCAI.

[7]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[8]  Tom Michael Mitchell,et al.  Model-directed learning of production rules , 1977, SGAR.

[9]  Judea Pearl,et al.  ON THE CONNECTION BETWEEN THE COMPLEXITY AND CREDIBILITY OF INFERRED MODELS , 1978 .

[10]  Tom M. Mitchell,et al.  MODEL-DIRECTED LEARNING OF PRODUCTION RULES1 , 1978 .

[11]  Donald Michie,et al.  Expert systems in the micro-electronic age , 1979 .

[12]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[14]  Donald Michie,et al.  Experiments on the Mechanization of Game-Learning. 2-Rule-Based Learning and the Human Window , 1982, Comput. J..

[15]  Richard A. O'Keefe.  Concept Formation From Very Large Training Sets , 1983, IJCAI.

[16]  B. A. Shepherd,et al.  An Appraisal of a Decision Tree Approach to Image Classification , 1983, IJCAI.

[17]  H. Simon,et al.  Rediscovering Chemistry with the Bacon System , 1983 .

[18]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[19]  Jaime G. Carbonell,et al.  An Overview of Machine Learning , 1983 .

[20]  Alen D. Shapiro,et al.  Role of structured induction in expert systems , 1983 .

[21]  R. Michalski,et al.  Learning from Observation: Conceptual Clustering , 1983 .

[22]  Ivan Bratko,et al.  Experiments in automatic learning of medical diagnostic rules , 1984 .

[23]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[24]  Donald Michie,et al.  CURRENT DEVELOPMENTS IN ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS , 1985 .

[25]  Claude Sammut,et al.  Concept development for Expert System Knowledge Bases , 1985, Australian Computer Journal.

[26]  J. R. Quilan.  Decision trees and multi-valued attributes , 1988 .

[27]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[28]  E. Stanford.  EXPERT SYSTEMS IN THE 1980 s , 2022 .