Abstract
Diagnosis is one of the most important processes in the medical field. Since the knowledge domains of clinical specialties are expanding rapidly in terms of complexity and volume of data, clinicians have, in many cases, difficulties to make an accurate diagnosis. Therefore, intelligent and quantitative support for diagnostic tasks can be effectively exploited for improving the effectiveness of the process and reduce misdiagnosis. In this respect, Multi-Classifier Systems represent one of the most promising approaches within Machine Learning methodologies. This paper proposes a Multi-Classifier Systems framework for supporting diagnostic activities with the aim of improving diagnostic accuracy. The framework uses and combines several classification algorithms by dynamically selecting the most competent classifier according to the test sample and its location in the feature space. Here, we extend our previous research. The new experimental results, compared with several multi classifier techniques, based on dynamic classifier selection, on classification datasets, show that the performance of the proposed framework exceeds the state-of-the-art dynamic classifier selection techniques.
Highlights
The diagnosis process has a remarkable impact within the medical sector, due to the wide scope and complexity of clinical knowledge domains
Among the several machine learning algorithms, we choose Support Vector Machines (SVM) [18], Multi-Layer Perceptron (MLP) [19], Naive Bayes (NB) [20], Decision Tree (DT) [21], and k-Nearest Neighbour (k-NN) [22], as they are widely used in different classification problems
The aim of the carried out computational experiments is to evaluate the proposed Multi-Classifier System (MCS) framework and assess whether the proposed approaches improve the classification task compared to other techniques in the literature
Summary
The diagnosis process has a remarkable impact within the medical sector, due to the wide scope and complexity of clinical knowledge domains. The novelty of the proposed approach is based on: (1) the local region of each test instance is defined dynamically; (2) the most competent classifier is selected by a procedure based on performance indexes evaluated on both local region and a specific set of instances. The accuracy is defined as the percentage of correct labelled instances in the local region belonging to the class assigned by the classifier to a given test instance Even in this case, the classifier that gets the highest accuracy is considered the most competent. The classifier that gets the highest accuracy is considered the most competent In both cases the local region is defined during the testing phase by the static k-NN algorithm and only one classifier is selected to perform the classification task.
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