Coin Classification Using a Novel Technique for Learning Characteristic Decision Trees by Controlling the Degree of Generalization

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Paul Davidsson
Title: Coin Classification Using a Novel Technique for Learning Characteristic Decision Trees by Controlling the Degree of Generalization
Conference name: Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems
Year: 1996
Pagination: 403-412
ISBN: 9056995243
Publisher: Gordon and Breach Science Publishers
City: Fukuoka; Japan
Organization: Blekinge Institute of Technology
Department: Dept. of Computer Science and Business Administration (Institutionen för datavetenskap och ekonomi)
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+46 455 780 00
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Authors e-mail: paul.davidsson@ide.hk-r.se
Language: English
Abstract: A novel method for learning characteristic decision trees is applied to the problem of learning the decision mechanism of coin-sorting machines. Decision trees constructed by ID3-like algorithms are unable to detect instances of categories not present in the set of training examples. Instead of being rejected, such instances are assigned to one of the classes actually present in the training set. To solve this problem the algorithm must learn characteristic, rather than discriminative, category descriptions. In addition, the ability to control the degree of generalization is identified as an essential property of such algorithms. A novel method using the information about the statistical distribution of the feature values that can be extracted from the training examples is developed to meet these requirements. The central idea is to augment each leaf of the decision tree with a subtree that imposes further restrictions on the values of each feature in that leaf.
Subject: Computer Science\Artificial Intelligence
Keywords: bank data processing, generalisation (artificial intelligence), learning by example, pattern classification, sorting, trees (mathematics)
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