{'fraction_non_alphanumeric': 0.011267605633802818, 'fraction_numerical': 0.0, 'mean_word_length': 5.7075471698113205, '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}}
GA-based feature subset selection: Application to Arabic speaker recognition system

Feature Selection is an important task which can affect the performance of pattern classification and recognition. In this paper, we present a feature selection algorithm based on genetic algorithm optimization. The algorithm adopts classifier performance and the number of the selected features as heuristic information, and selects the optimal feature subset in terms of feature set size and classification performance. Experimental results on various speakers show that our algorithm can obtain better classification accuracy with a smaller feature set which is crucial for real time application and low resources devices.