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.

Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art. Keywords: classification, tree-structured classifiers, data compaction 1. Introduction Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data exploration techniques. Enormous amounts of data are being collected daily from major scientific projects e.g., Human Genome...

This paper presents a heuristic, attribute-based, noise-tolerant data mining program, HCV (Version 2.0), based on the newly-developed extension matrix approach. By dividing the positive examples (PE) of a specific class in a given example set into intersecting groups and adopting a set of strategies to find a heuristic conjunctive formula in each group which covers all the group's positive examples and none of the negative examples (NE), the HCV induction algorithm adopted in the HCV (Version 2.0) software finds a description formula in the form of variable-valued logic for PE against NE in low-order polynomial time at induction time. In addition to the HCV induction algorithm, this paper also outlines some of the techniques for noise handling and discretization of numerical domains developed and implemented in the HCV (Version 2.0) software, and provides a performance comparison of HCV (Version 2.0) with other data mining algorithms ID3, C4.5, C4.5rules and NewID in noisy and continuo...