{'fraction_non_alphanumeric': 0.011306532663316583, 'fraction_numerical': 0.0, 'mean_word_length': 5.586776859504132, '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}}
Feature subset selection using generalized steepest ascent search algorithm

This paper presents a novel generalized steepest ascent algorithm for selecting a subset of features. Our proposed algorithm is an improvement upon the prior steepest ascent algorithm by selecting a better starting search point and performing a more thorough search than the steepest ascent algorithm. For any given criterion function used to evaluate the effectiveness of a selected feature subsets, our method is guaranteed to provide solutions that equal or exceed those of the state-of-the-art sequential forward floating selection algorithm. Experimental results for two real data sets confirm that our algorithm consistently selects better subsets than other well-known suboptimal feature selection algorithms do.