{'fraction_non_alphanumeric': 0.01580246913580247, 'fraction_numerical': 0.0019753086419753087, 'mean_word_length': 5.514469453376206, '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}}
Evolutionary Feature Subset Selection for Pattern Recognition Applications

A crucial part of a typical pattern recognition system is the extraction of the appropriate information that uniquely describes the patterns under processing. This information has the form of vectors and their contents are called features, which are constructed by specific extraction methods (Feature Extraction Methods FEMs). The length of the extracted feature vectors may take high dimension by incorporating many features for each pattern, although this huge information may be redundant and in a lot of cases this extra information corrupts the separability of the patterns under recognition. Therefore the need of an additional pre-processing method that reduces the feature vectors’ dimension, by selecting the most appropriate features, subject to some performance indices (class separability, high classification error etc.) is necessary. This procedure is called dimensionality reduction or feature subset selection and has attracted the attention of the scientific community for the last thirty years (Molina et al., 2002). This chapter is focused on the usage of evolutionary methods in selecting the appropriate feature subset from a pool of features, in a way the resulted subset increases the recognition rates in several benchmark pattern recognition problems. A simple genetic algorithm is used to examine the usefulness of a predefined feature set of some benchmark problems from the literature and some useful conclusions about the ability of these features to recognize the patterns are drawn. Moreover, the dependency of the resulted feature subsets, as far as their classification abilities are concerned, on the form of the fitness function used to measure the appropriateness of the candidate solutions, constructed by the genetic algorithm, is studied in this chapter. Three fitness functions with different properties are examined and their performance is compared to each other, for a set of pattern recognition problems.