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Stability or robustness is a crucial yardstick for analyzing and evaluating feature selection
algorithms which have become indispensible due to unprecedented advancements in knowledge data
discovery and management. Stability of feature selection algorithms is taken as the insensitivity of the
algorithm to perturbations in the training data with reference to the performance of the algorithm with
all training data. In this work, we propose an algorithm for evaluating and quantifying the robustness
of feature ranking algorithms and test three feature ranking algorithms: relief, diff-criterian and
mutual information on four different real life binary data sets from text mining, handwriting
recognition, medical diagnoses and medicinal sciences. We then analyze the stability profiles of
feature selectors and determine how stability is a desirable characteristic of a feature ranking
algorithm. We find that diff-criterian, and mutual information, outperform relief in stability.
Aqsa Shabbir, Kashif Javed, Yasmin Ansari, Haroon A Babri. (2014) Stability of Feature Ranking Algorithms on Binary Data, Pakistan Journal of Engineering and Applied Sciences, VOLUME 15, Issue 1.
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