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Coin Classification Using a Novel Technique for Learning Characteristic Decision Trees by Controlling the Degree of Generalization
Responsible organisation
1996 (English)Conference paper, (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Fukuoka; Japan: Gordon and Breach Science Publishers , 1996.
Keyword [en]
bank data processing, generalisation (artificial intelligence), learning by example, pattern classification, sorting, trees (mathematics)
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-9335Local ID: oai:bth.se:forskinfoFF10FD361360E6EEC12568A3002CAB3EISBN: 9056995243 (print)OAI: oai:DiVA.org:bth-9335DiVA: diva2:837147
Conference
Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems
Available from: 2012-09-18 Created: 2000-03-15 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • ieee
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Language
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  • en-GB
  • en-US
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Output format
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