caa a22 4500 605477957 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-015-5508-x doi (NATIONALLICENCE)springer-10.1007/s10994-015-5508-x Probabilistic combination of classification rules and its application to medical diagnosis [Elektronische Daten] [Jakub Tomczak, Maciej Zięba] Application of machine learning to medical diagnosis entails facing two major issues, namely, a necessity of learning comprehensible models and a need of coping with imbalanced data phenomenon. The first one corresponds to a problem of implementing interpretable models, e.g., classification rules or decision trees. The second issue represents a situation in which the number of examples from one class (e.g., healthy patients) is significantly higher than the number of examples from the other class (e.g., ill patients). Learning algorithms which are prone to the imbalance data return biased models towards the majority class. In this paper, we propose a probabilistic combination of soft rules, which can be seen as a probabilistic version of the classification rules, by introducing new latent random variable called conjunctive feature. The conjunctive features represent conjunctions of values of attribute variables (features) and we assume that for given conjunctive feature the object and its label (class) become independent random variables. In order to deal with the between class imbalance problem, we present a new estimator which incorporates the knowledge about data imbalanceness into hyperparameters of initial probability of objects with fixed class labels. Additionally, we propose a method for aggregating sufficient statistics needed to estimate probabilities in a graph-based structure to speed up computations. At the end, we carry out two experiments: (1) using benchmark datasets, (2) using medical datasets. The results are discussed and the conclusions are drawn. The Author(s), 2015 Probabilistic combination nationallicence Classification rules nationallicence Imbalanced data nationallicence Medical diagnosis nationallicence Tomczak Jakub Department of Computer Science, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut Zięba Maciej Department of Computer Science, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 105-135 0885-6125 101:1-3<105 2015 101 10994 https://doi.org/10.1007/s10994-015-5508-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5508-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tomczak Jakub Department of Computer Science, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut NATIONALLICENCE 700 1- Zięba Maciej Department of Computer Science, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 105-135 0885-6125 101:1-3<105 2015 101 10994