## Constructing X-of-N Attributes for Decision Tree Learning (1998)

Venue: | Machine Learning |

Citations: | 19 - 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}

}

### Years of Citing Articles

### OpenURL

### 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...