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In this paper, the concept of the frequently covered points (FCP) and the infrequently covered points (ICP) is presented. By means of the cutpoints sieve method, we can rapidly pick out the corresponding cutpoints of ICP, namely preferred cutpoints, from all pending cutpoints of interval attributes. And then, only preferred cutpoints are used for computing information entropy of partition (IEP). Finally,...
In this paper, we propose a methodology consists of several unsupervised clustering techniques to acquire a satisfactory segmentation of computed tomography (CT) brain images. The ultimate goal of segmentation is to obtain three segmented images, which are the abnormalities, cerebrospinal fluid (CSF) and brain matter respectively. The proposed approach contains of two phase-segmentation methods. In...
A novel method of rule extraction from artificial neural network with optimized activation function is proposed. Weight-decay approach is used in training and the unnecessary connections in the neural network are pruned at the cost of an increase in the error function within a predetermined limit. A penalty term is added in the activation function to facilitate the values of hidden and output nodes...
In the medical diagnosis, the false negative prediction is more serious than the false positive prediction. We introduce the cost-sensitive rule ensemble method (RuleFit) to breast ultrasound, which can induce the interpretable scoring rules for malignancy assessment, and can be applied to tune the sensitivity and specificity of the predictive model by varying the cost weights of misclassification...
Coordination and negotiation among agents are necessary when multiple agents are motivated to make a diagnosis for a patient together. In this paper a model of a multi-agent diagnosis helping system (MADHS) is given, where several knowledge-based systems are considered as cooperative agents in medical diagnoses. Fuzziness and uncertainty have been incorporated into decision trees to form the reasoning...
Inductive learning is an efficient way to construct knowledge from the observation of a set of cases. It rises from the particular to the general and it provides a system with the capacity of finding by itself any useful knowledge to handle forthcoming cases. Given a set of observed cases (a so-called training set), an inductive learning algorithm is able to construct a more complex knowledge base...
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