Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy"

This are the feature values used in the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy". The dataset regroups feature values for every "cheap" features available in the R package <em>flacco </em>and are computed using 5 sampling strategies and in dimension \($d=5$\): Random: the classical Mersenne-Twister algorithm; Randu: a random number generator that is notoriously bad; LHS: a centered Latin Hypercube Design; iLHS: an improved Latin Hypercube Design; Sobol: points extracted from a Sobol' low-discrepancy sequence. The csv file <em>features_summury_dim_5_ppsn.csv </em>regroups 100 values for every features whereas <em>features_summury_dim_5_ppsn_median.csv </em>regroups for every feature the median of the 100 values. In the folder <em>PPSN_feature_plots</em> are the histograms of feature values on the 24 COCO functions for 3 sampling strategies: Random, LHS and Sobol. The Python file <em>sampling_ppsn.py</em> is the code used to generate the sample points from which the feature values are computed. The file <em>stats50_knn_dt.csv</em> provide the raw data of median and IQR (inter quartile interval) for the heatmaps and boxplots available in the paper. Finally, the files <em>results_classif_knn100.csv</em> (resp. dt) provide the accuracy of 100 classifications for every settings.