The development of deep convolutional neural network architecture is critical to the improvement image classification task performance. Many studies use and focus on modifying structure improve Conversely, our study focuses loss function design. Cross-entropy Loss (CEL) has been widely used for training multi-class classification. Although CEL successfully implemented in several tasks, it only ...

Feature selection is an important task in data-driven control applications to identify relevant features and remove non-informative ones, for example residual fault diagnosis. For multi-class data, the objective find a minimal set of that can distinguish data from all different classes. A distributed feature algorithm derived using convex optimization Alternating Direction Method Multipliers. T...

Colorectal image classification is a novel application area in medical processing. images are one of the most prevalent malignant tumour disease type world. However, due to complexity histopathological imaging, accurate and effective still needs be addressed. In this work we proposed architecture convolution neural network with deep learning models for multiclass histopathology images. We achie...

With the emergence of rich online content, efficient information retrieval systems are required. Application content includes rich text, speech, still images and videos. This content, either stored or queried, can be assigned to many classes or labels at the same time. This calls for the use of multi-label classification techniques. In this paper, a new kernel-basedmulti-label classification al...

Using hierarchies of classes is one the standard methods to solve multi-class classification problems. In literature, selecting right hierarchy considered play a key role in improving performance. Although different have been proposed, there still lack understanding what makes good and method extract perform better or worse. To this effect, we analyze compare some most popular approaches extrac...

Real-time disease prediction has emerged as the main focus of study in field computerized medicine. Intelligent identification framework can assist medical practitioners diagnosing a way that is reliable, consistent, and timely, successfully lowering mortality rates, particularly during endemics pandemics. To prevent this pandemic’s rapid widespread, it vital to quickly identify, confine, treat...

Error-Correcting Output Coding (ECOC) is a general framework for multiclass text classification with a set of binary classifiers. It can not only help a binary classifier solve multi-class classification problems, but also boost the performance of a multi-class classifier. When building each individual binary classifier in ECOC, multiple classes are randomly grouped into two disjoint groups: po...

Several researchers have proposed effective approaches for binary classification in the last years. We can easily extend some of those techniques to multi-class. Notwithstanding, some other powerful classifiers (e.g., SVMs) are hard to extend to multi-class. In such cases, the usual approach is to reduce the multi-class problem complexity into simpler binary classification problems (divide-and-...