{'fraction_non_alphanumeric': 0.0106951871657754, 'fraction_numerical': 0.0, 'mean_word_length': 5.6117647058823525, '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}}
Class imbalance problem using a hybrid ensemble approach

This paper proposes a comparative study that investigates the effects of using resampling (undersampling and over- sampling) methods with homogenous ensemble methods Bagging and AdaBoost in imbalanced data sets. We presented a hybrid ensemble approach that combined multi resampling by integrating both undersampling and oversampling to get benefits and reduces drawbacks caused by each of them. The proposed approach has improved the performance even those most sensitive to imbalanced class data sets.