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The paper reports an experimentally identified list of benchmark data setsthat are hard for representative classification and feature selection methods.This was done after systematically evaluating a total of 48 combinations ofmethods, involving eight state-of-the-art classification algorithms and sixcommonly used feature selection methods, on 129 data sets from the UCIrepository (some data sets with known high classification accuracy wereexcluded). In this paper, a data set for classification is called hard if noneof the 48 combinations can achieve an AUC over 0.8 and none of them can achievean F-Measure value over 0.8; it is called easy otherwise. A total of 15 out ofthe 129 data sets were found to be hard in that sense. This paper also comparesthe performance of different methods, and it produces rankings ofclassification methods, separately on the hard data sets and on the easy datasets. This paper is the first to rank methods separately for hard data sets andfor easy data sets. It turns out that the classifier rankings resulting fromour experiments are somehow different from those in the literature and hencethey offer new insights on method selection. It should be noted that the RandomForest method remains to be the best in all groups of experiments.

Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection