Constructing nominal X-of-N attributes

Authors:
Zijian Zheng
Affiliations:
Basser Department of Computer Science, The University of Sydney, NSW, Australia
Venue:
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Year:
1995

Citing 7
Cited 7

Scientific discovery: computational explorations of the creative process

Scientific discovery: computational explorations of the creative process
Inferring decision trees using the minimum description length principle

Information and Computation
A comparative study of ID3 and backpropagation for English text-to-speech mapping

Proceedings of the seventh international conference (1990) on Machine learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments

Machine Learning - Special issue on evaluating and changing representation
On Changing Continuous Attributes into Ordered Discrete Attributes

EWSL '91 Proceedings of the European Working Session on Machine Learning
Multivariate Versus Univariate Decision Trees

Multivariate Versus Univariate Decision Trees

Constructing X-of-N Attributes for Decision Tree Learning

Machine Learning
Feature Generation Using General Constructor Functions

Machine Learning
Attribute generation based on association rules

Knowledge and Information Systems
Simplifying decision trees: A survey

The Knowledge Engineering Review
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Two-stage phone duration modelling with feature construction and feature vector extension for the needs of speech synthesis

Computer Speech and Language
Unsupervised feature construction for improving data representation and semantics

Journal of Intelligent Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called X-of-N, that constructs new nominal attributes in the form of X-of-N representations An X-of-N is a Bet 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.