---
title: "Constructing Nominal X-of-N Attributes"
authors:
  - "Zijian Zheng"
doi: ""
url: "https://ijcai.org/proceedings/1995/138"
pdf: "https://ijcai.org/proceedings/1995/0138.pdf"
venue: "Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence"
year: 1995
pages: "1064-1069"
track: "Learning"
keywords:
---

# Constructing Nominal X-of-N Attributes

Zijian Zheng

*Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence*, pages 1064-1069.

## Abstract

Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of X-of-N representations. An X-of-N is a set containing one or more attribute-value pairs. For a given instance, its value corresponds to the number of its attribute-value pairs that are true. The promising preliminary experimental results, on both artificial and real-world domains, show that constructing new nominal attributes in the form of X-of-N representations can significantly improve the performance of selective induction in terms of both higher prediction accuracy and lower theory complexity.