Effect of classifiers in consensus feature ranking for biomedical datasets

Authors:
Shobeir Fakhraei;Hamid Soltanian-Zadeh;Farshad Fotouhi;Kost Elisevich
Affiliations:
Wayne State University, Detroit, MI, & Henry Ford Health System Detroit, MI, USA;Henry Ford Health System, Detroit, MI, & University of Tehran, Tehran, Iran;Wayne State University, Detroit, MI, USA;Henry Ford Health System, Detroit, MI, USA
Venue:
DTMBIO '10 Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
Year:
2010

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Abstract

Many informative aspects of medical datasets may be extracted from comparative study of features discriminative power. Recently, consensus feature rankings have been proposed to achieve robust, unbiased and reliable rankings of attributes. We have studied the effect of classifier inclusion in a consensus feature ranking method for a medical dataset with missing values and class imbalanced data. Ability of consensus feature rankings to demonstrate superior performance with unseen classifiers is also studied in this paper.