{'fraction_non_alphanumeric': 0.015356820234869015, 'fraction_numerical': 0.0, 'mean_word_length': 5.674698795180723, '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}}
Selection of voice features to diagnose hearing impairments of children

Real-world medical data is often heterogeneous, containing many cases and features, each of which requires different a type of processing. Generally, this means that the subsets of relevant features are different for various cases. The set of voice descriptors in the problem of hearing impairment diagnosis is an example of such a heterogeneous domain. Ensemble feature selection techniques are adopted to take into account the data heterogeneity. This paper analyses the applicability of various feature selection approaches in diagnosing hearing impairments in the context of an ensemble classification. Ensemble feature selection produces multiple classifiers for this domain, based on feature subsets derived by different feature selection approaches. In particular, we are interested in performing feature selection for each particular case, taking into consideration any hidden heterogeneity in the data. We use real-world clinical hearing impairment data and compare ensemble classification to the single-classifier technique.