STochFS: a framework for combining feature selection outcomes through a stochastic process

Authors:
Jerffeson Teixeira de Souza;Nathalie Japkowicz;Stan Matwin
Affiliations:
Computer Science Department, Federal University of Ceará, Fortaleza, Brazil;School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

Citing 8
Cited 2

A practical approach to feature selection

ML92 Proceedings of the ninth international workshop on Machine learning
Bagging predictors

Machine Learning
Feature selection for high-dimensional genomic microarray data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ADHOC: a Tool for Performing Effective Feature Selection

ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Using learning to facilitate the evolution of features for recognizing visual concepts

Evolutionary Computation
Learning with many irrelevant features

AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2

Software measurement data reduction using ensemble techniques

Neurocomputing
Identification of micro RNA biomarkers for cancer by combining multiple feature selection techniques

Journal of Computational Methods in Sciences and Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Feature Selection problem involves discovering a subset of features such that a classifier built only with this subset would have better predictive accuracy than a classifier built from the entire set of features. Ensemble methods, such as Bagging and Boosting, have been shown to increase the performance of classifiers to remarkable levels but surprisingly have not been tried in other parts of the classification process. In this paper, we apply the ensemble approach to feature selection by proposing a systematic way of combining various outcomes of a feature selection algorithm. The proposed framework, named STochFS, have been shown empirically to improve the performance of well-known feature selection algorithms.