ReliefMSS: a variation on a feature ranking ReliefF algorithm

Authors:
Salim Chikhi;Sadek Benhammada
Affiliations:
Computer Science Department, Mentouri University, MISC Laboratory, route de Aïn El Bey, Constantine 25017, Algeria.;Computer Science Department, Mentouri University, MISC Laboratory, route de Aïn El Bey, Constantine 25017, Algeria
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2009

Citing 9
Cited 0

A practical approach to feature selection

ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF

Machine Learning
Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A selective sampling approach to active feature selection

Artificial Intelligence
Iterative RELIEF for feature weighting

ICML '06 Proceedings of the 23rd international conference on Machine learning
Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets

International Journal of Business Intelligence and Data Mining
Comprehensible evaluation of prognostic factors and prediction of wound healing

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Relief algorithms are successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation. In this paper, we propose a variant of ReliefF algorithm: ReliefMSS. We analyse the ReliefMSS parameters and compare ReliefF and ReliefMSS performances as regards the number of iterations, the number of random attributes, the noise effect, the number of nearest neighbours and the number of examples presented. We find that for the most of these parameters, ReliefMSS is better than ReliefF.