{'fraction_non_alphanumeric': 0.014360313315926894, 'fraction_numerical': 0.0, 'mean_word_length': 5.445378151260504, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Using a feature selection ensemble on DNA microarray datasets

DNA microarray has brought a difficult challenge for researchers due to the high number of gene expression contained and the small samples size. Therefore, feature selection has become an indispensable preprocessing step. In this paper we propose an ensemble for feature selection based on combining rankings of features. The individual rankings are combined with different aggregation methods, and a practical subset of features is selected according to a data complexity measure –the inverse of Fisher discriminant ratio–. The proposed ensemble, tested on seven different DNA microarray datasets using a Support Vector Machine as classifier, was able to obtain the best results in different scenarios.