{'fraction_non_alphanumeric': 0.01675977653631285, 'fraction_numerical': 0.0, 'mean_word_length': 5.345132743362832, '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}}
A feature-weight detector neural network and its application

A feedback neural network model with memory connections for classification and weight connections for selection is proposed. After training, a memory vector is interpreted as a prototype of a feature pattern, and a weight vector represents importance of feature variables to the corresponding feature pattern. The proposed neural network has a simple network architecture and high learning speed. Moreover, the obtained knowledge can be described by natural language. The technique is applied to the IRIS data: the two effective feature variables were extracted, and the corresponding number of errors, is almost the same as using four feature variables.