{'fraction_non_alphanumeric': 0.014586709886547812, 'fraction_numerical': 0.0, 'mean_word_length': 4.885714285714286, '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}}
Probabilistic Approach for Reduction of Irrelevant Tree-structured Data

This article aims at pruning noisy or irrelevant subtrees in a set of trees. The originality of this approach, in comparison with classic techniques in prototype selection, comes not from the non-deletion of the whole tree, but rather of some of its subtrees. Our method is based on the computation of confidence intervals on a set of subtrees according to a probability distribution. We propose an approach to assess these intervals on this specific type of data and show experimentally its interest in the context of learning from noisy data.