Robust feature selection technique using rank aggregation

Chandrima Sarkar, Sarah Cooley, Jaideep Srivastava

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique that produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases, whereas classifiers exploit different statistical properties of data for evaluation. In numerous situations, this can put researchers into dilemma with regard to which feature selection method and classifiers to choose from a vast range of choices. In this article, we propose a technique that aggregates the consensus properties of various feature selection methods in order to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable toward achieving similar and, ideally, higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the robustness index (RI). We perform an extensive empirical evaluation of our technique on eight datasets with different dimensions, including arrythmia, lung cancer, Madelon, mfeat-fourier, Internet ads, leukemia-3c, embryonal tumor, and a real-world dataset, vis., acute myeloid leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that, compared with other techniques, our algorithm improves the classification accuracy by approximately 3-4% in a dataset with fewer than 500 features and by more than 5% in a dataset with more than 500 features, across a wide range of classifiers. © 2014

Original languageEnglish (US)
Pages (from-to)243-257
Number of pages15
JournalApplied Artificial Intelligence
Issue number3
StatePublished - Mar 16 2014

Bibliographical note

Funding Information:
AML data resource in this work was supported by the National Institutes of Health/NCI grant P01 111412, PI Jeffrey S. Miller, M.D, utilizing the Masonic Cancer Center, University of Minnesota Oncology Medical Informatics shared resources. We would like to thank Atanu Roy for his critical reviews and technical feedback during the development of this research.


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