Features of Decision Trees as a Technique of Knowledge Modeling.

Richard F. Bonner, Violetta Galant, Mieczyslaw L. Owoc: Features of Decision Trees as a Technique of Knowledge Modeling CSIT 1999 : 135-137

Abstract

One of most promising techniques of knowledgebase modeling today is machine learning, whereby decision trees play an important role in expressing knowledge by inductive methods. It is known, however, that desion tree methods have weak points. One of potential problems lies in the heterogenity of distribution classification attributes: classes with few instances yield poor description and may disappear alltogether. Another problem concerns the treatment of new instances during learning, so noise is not confused with data announcing a class of interest. The paper discusses these and other problems, in particular comparing criteria for growing decision trees. The framework of analysis is that of stochastic control theory and algorithmic complexity.

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Printed Edition

Ch. Freytag and V. Wolfengagen (Eds.): CSIT'99, Proceedings of 1st International Workshop on Computer Science and Information Technologies, January 18-22, 1999, Moscow, Russia. MEPhI Publishing 1999, ISBN 5-7262-0263-5

Electronic Edition