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According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer...
Kernel fuzzy clustering has been applied to data with nonlinear relationships. Two approaches were used: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesn't work well with “multiple-density” clusters, Multiple Kernel Fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the...
Intuitionistic fuzzy edge detection algorithm has been used for the signification or characterization of images. It has been designed by experts and the algorithm provides to aim to minimize errors. However, it has a fixed value for thresholding. In this paper, a hybrid algorithm has been developed using the Otsu method which is calculated a threshold value depending on the images. To be applicable...
A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation...
With the aim of a better generalizing performance than standard SVMs by assigning a fuzzy membership degree to each input point in the classification problem, Fuzzy Support Vector Machines (FSVMs) have been widely applied and have shown great success in various applications. However, the success of such a machine learning technique is inherently limited when applied to the problem of class imbalance...
We propose the technique of the semi-automatic image creation. By this we mean an automatic completion of an image that is partially defined on the given domain. The essential feature of this technique is that the complementary area is much larger than that where the image is defined. Moreover, the proposed technique can be used in image upsampling, image inpainting, etc. In this contribution, we...
We present Rough-Fuzzy Support Vector Domain Description (RFSVDD), a novel data description algorithm that provides a rough-fuzzy characterization of a data set and shows its potential for outlier detection. Its resulting data structure is characterized by two components: a crisp lower-approximation and a fuzzy boundary. While the lower-approximation consists of those data points that lie inside the...
Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
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