{'fraction_non_alphanumeric': 0.01678183613030602, 'fraction_numerical': 0.006910167818361303, 'mean_word_length': 5.945205479452055, '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}}
Nearest Neighbor Classifier Based on Nearest Feature Decisions

High feature dimensionality of realistic datasets adversely affects the recognition accuracy of nearest neighbor (NN) classifiers. To address this issue, we introduce a nearest feature classifier that shifts the NN concept from the global-decision level to the level of individual features. Performance comparisons with 12 instance-based classifiers on 13 benchmark University of California Irvine classification datasets show average improvements of 6 and 3.5% in recognition accuracy and area under curve performance measures, respectively. The statistical significance of the observed performance improvements is verified by the Friedman test and by the post hoc Bonferroni–Dunn test. In addition, the application of the classifier is demonstrated on face recognition databases, a character recognition database and medical diagnosis problems for binary and multi-class diagnosis on databases including morphological and gene expression features.