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Coin classification using a novel technique for learning characteristic decision trees by controlling the degree of generalization

Davidsson, Paul (1996) In Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-96) p.403-412
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... (More)
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. (Less)
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author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
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in
Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-96)
pages
403 - 412
publisher
Gordon and Breach Science Publishers
language
English
LU publication?
no
id
bbbc1036-49ce-4da3-a07d-ab79429463d5 (old id 526569)
alternative location
http://fileadmin.cs.lth.se/ai/psfiles/IEAAIE96.pdf
date added to LUP
2007-09-13 15:44:22
date last changed
2016-06-29 09:12:48
@inproceedings{bbbc1036-49ce-4da3-a07d-ab79429463d5,
  abstract     = {A novel method for learning characteristic decision trees<br/><br>
is applied to the problem of learning the decision mechanism<br/><br>
of coin-sorting machines. Decision trees constructed by ID3-like algorithms are unable to detect instances of categories<br/><br>
not present in the set of training examples. Instead of being rejected, such instances are assigned to one of the<br/><br>
classes actually present in the training set. To solve this<br/><br>
problem the algorithm must learn characteristic, rather than<br/><br>
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.},
  author       = {Davidsson, Paul},
  booktitle    = {Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-96)},
  language     = {eng},
  pages        = {403--412},
  publisher    = {Gordon and Breach Science Publishers},
  title        = {Coin classification using a novel technique for learning characteristic decision trees by controlling the degree of generalization},
  year         = {1996},
}