Coin classification using a novel technique for learning characteristic decision trees by controlling the degree of generalization
(1996) 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/526569
- author
- Davidsson, Paul
- publishing date
- 1996
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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
- 2016-04-04 11:27:40
- date last changed
- 2018-11-21 21:04:59
@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}},
url = {{http://fileadmin.cs.lth.se/ai/psfiles/IEAAIE96.pdf}},
year = {{1996}},
}