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Constructing X-of-N Attributes for Decision Tree Learning (1998)
| Venue: | Machine Learning |
| Citations: | 20 - 0 self |
BibTeX
@INPROCEEDINGS{Zheng98constructingx-of-n,
author = {Zijian Zheng},
title = {Constructing X-of-N Attributes for Decision Tree Learning},
booktitle = {Machine Learning},
year = {1998},
pages = {1--43},
publisher = {Kluwer Academic}
}
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Abstract
. While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, X-of-N. An X-of-N representation is a set containing one or more attribute-value pairs. For a given instance, the value of an X-of-N representation corresponds to the number of its attribute-value pairs that are true of the instance. A single X-of-N representation can directly and simply represent any concept that can be represented by a single conjunctive, a single disjunctive, or a single M-of-N representation commonly used for constructive induction, and the reverse is not true. In this paper, we describe a constructive decision tree learning algorithm, called XofN. When building decision trees, this algorithm creates one X-of-N representation, either as a nominal attribute or as a numeric attribute, at each decision node. The construction of X-of-N representations is carrie...
Keyphrases
x-of-n attribute decision tree learning x-of-n representation attribute-value pair numeric attribute constructive decision tree new binary attribute single m-of-n representation single disjunctive decision tree many constructive induction algorithm focus single conjunctive nominal attribute novel method new constructive operator decision node constructive induction x-of-n representation corresponds single x-of-n representation
