Outlier detection, i.e., the task of detecting points that are markedly different from data sample, is an important challenge in machine learning. When a model built, these special can skew training and result less accurate predictions. Due to this fact, it identify remove them before building any supervised often first step when dealing with learning problem. Nowadays, there exists very large ...

There is presently no unified methodology that allows the evaluation of supervised or non-supervised classification algorithms. Supervised problems are evaluated through quality functions while non-supervised problems are evaluated through several structural indexes. In both cases a lot of useful information remains hidden or is not considered by the evaluation method, such as the quality of th...

Conventional image classification methods restricts each pixel of data set to exclusively just one cluster. As a consequence, with this approach the classification results are often very crispy, i.e., each pixel of the image belongs to exactly just one class. However, in many real situations, for images, issues such as limited spatial resolution, poor contrast, overlapping intensities, and nois...