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Multiclassifier systems applied to the computer-aided sequential medical diagnosis

Kurzyński M., Majak M., Żołnierek A.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 4

619--625

The diagnosis of patient's state based on results of successive examinations is common task in the medicine. In computer-aided algorithms taking into account the patient's history in order to improve the quality of classification seems to be very reasonable solution. In this study, two original multiclassifier systems (MC) for the computer-aided sequential diagnosis are developed, which differ with decision scheme and the methods of combining of base classifiers. The first MC system is based on dynamic ensemble selection scheme and works in two-level structure. The second MC system in combining procedure uses original concept of meta-Bayes classifier and produces decision according to the Bayes rule. Both MC systems were practically applied to the diagnosis of human acid–base equilibrium states and compared with some state-of-the-art sequential diagnosis methods. Results obtained in experimental investigations imply that MC system is effective approach, which improves recognition accuracy in sequential diagnosis scheme.

Dynamical ensemble selection - experimental analysis on homogenous pool of classifiers

Baczyńska P., Burduk R.

Journal of Medical Informatics & Technologies

2015

Vol. 24

107--112

The paper presents the dynamic ensemble selection based on the analysis of the decision proﬁles. These proﬁles are obtained from a posteriori probability functions returned from the base classifiers during the training process. Presented in the paper dynamic ensemble selection algorithms are dedicated to the binary classification task. In order to verify these algorithms, a number of experiments have been carried out on several medical data sets. The proposed dynamic ensemble selection is experimentally compared against the ensemble with the sum fusion method. As base classifiers we used the pool of homogeneous classifiers. The obtained results are promising because we could improve the classification accuracy of the ensemble classifier.