{'fraction_non_alphanumeric': 0.01977401129943503, 'fraction_numerical': 0.0, 'mean_word_length': 5.8173076923076925, '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}}
Incorporating an EM-approach for handling missing attribute-values in decision tree induction

Data with missing attribute-values are quite common in many classification problems. In this paper, we incorporate an expectation-maximization (EM) inspired approach for filling up missing values to decision tree learning with the objective of improving classification accuracy. Here, each missing attribute-value is iteratively filled using a predictor constructed from the known values and predicted values of the missing attribute-values from the previous iteration. We show that our approach significantly outperforms some standard machine learning methods for handling missing values in classification tasks.