This paper deals with ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) methods including VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative distance-based assessment (CODAS). The novalty of this paper is to design the three MCDM algorithms based on q-rung orthopair hesitant fuzzy sets with different distance and similarity measures. The well known distance and similarity measures are to be taken such as Hausdorff measure, hybrid Hausdorff and distance measure, synergetic measure, similarity for Hausdorff measure, similarity for hybrid Hausdorff and distance measure, similarity for synergetic measure, ordered Hausdorff measure, ordered hybrid Hausdorff and distance measure, similarity for ordered Hausdorff measure and similarity for ordered hybrid Hausdorff and distance measure This is the first time in the literature, an ensemble feature selection problem is modeled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR, TOPSIS and CODAS techniques with distance and similarity measures. By using q-ROHFS VIKOR, TOPSIS and CODAS methods, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features, from which the user can select the desired number of features. To prove the efficiency and optimality of our proposed method, we compared with the basic filter-based feature selections and ensemble feature selection by using feature ranking strategy. Our method is superior and efficient than the ensemble methods based on the accuracy and F-score levels.