Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Automatic web pages categorization with ReliefF and Hidden Naive Bayes
Proceedings of the 2007 ACM symposium on Applied computing
Automatic Web Page Classification Using Various Features
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
ReliefMSS: a variation on a feature ranking ReliefF algorithm
International Journal of Business Intelligence and Data Mining
Application of emerging patterns for multi-source bio-data classification and analysis
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and x^2-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.