{'fraction_non_alphanumeric': 0.01141352063213345, 'fraction_numerical': 0.0, 'mean_word_length': 5.785714285714286, '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}}
Correlation based feature selection method

Feature selection is an important data preprocessing step which is performed before a learning algorithm is applied. The issue that has to be taken into consideration when proposing a feature selection method is its computational complexity. Often, if the feature selection process is fast, it cannot thoroughly search the feature subset space and classification accuracy is degraded. Lately, a pairwise feature selection method was proposed as an effective trade-off between computation speed and classification accuracy. In this paper, a new feature selection method is proposed which further improves feature selection speed while preserving classification accuracy. The new method selects features individually or in a pairwise manner based on the correlations between features. Experiments conducted on several benchmark data sets prove with high statistical significance that the correlation-based feature selection method shortens computations compared to the pairwise feature selection method and produces classification errors that are not worse than those produced by existing methods.