{'fraction_non_alphanumeric': 0.01473922902494331, 'fraction_numerical': 0.0, 'mean_word_length': 5.6893939393939394, '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}}
Affinity propagation enhanced by estimation of distribution algorithms

Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.