Generation of globally relevant continuous features for classification

Authors:
Sylvain Létourneau;Stan Matwin;A. Fazel Famili
Affiliations:
Institute for Information Technology, National Research Council Canada, Ottawa;School of Information Technology and Engineering, University of Ottawa, Canada and Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland;Institute for Information Technology, National Research Council Canada, Ottawa
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

Citing 12
Cited 0

Boolean Feature Discovery in Empirical Learning

Machine Learning
Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Learning hard concepts through constructive induction: framework and rationale

Computational Intelligence
Attribute selection for modelling

Future Generation Computer Systems - Special double issue on data mining
Understanding the Crucial Role of AttributeInteraction in Data Mining

Artificial Intelligence Review
Data-Driven Constructive Induction

IEEE Intelligent Systems
Induction of Decision Trees

Machine Learning
Induction of Recursive Bayesian Classifiers

ECML '93 Proceedings of the European Conference on Machine Learning
A Normalization Method for Contextual Data: Experience from a Large-Scale Application

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Global Data Analysis and the Fragmentation Problem in Decision Tree Induction

ECML '97 Proceedings of the 9th European Conference on Machine Learning
Representational transformation through constructive induction

Representational transformation through constructive induction
A system for induction of oblique decision trees

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

All learning algorithms perform very well when provided with a small number of highly relevant features. This paper proposes a constructive induction method to automatically construct such features. The method, named GLOREF (GLObally RElevant Features), exploits low-level interactions between the attributes in order to generate globally relevant features. The usefulness of the approach is demonstrated empirically through a large scale experiment involving 13 classifiers and 24 datasets. Results demonstrate the ability of the method in generating highly informative features and a strong positive effect on the accuracy of the classifiers.