{'fraction_non_alphanumeric': 0.046296296296296294, 'fraction_numerical': 0.003703703703703704, 'mean_word_length': 5.078651685393258, '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 diagnostic system based on a multi-decision approximate rules model

Modified Rough Sets (MRS)-based diagnostic systems eliminated many of the limitations of Rough Sets (RS)-based, statistical-based, and decision tree-based systems and because of that, they have a better performance. In contrast with the other systems, MRS-based diagnostic systems have potential to handle Multi-Decision Approximate (MDA) rules. In this paper, we (1) develop a diagnostic model based on MDA rules and (2) evaluate the classification power of the model.