In recent years, studies in the field of deep learning have made significant progress. These focusedmore on datasets with balanced classification, and less research has been done imbalanced datasets, whichare great importance real world present challenges for classification. This articlestudies problem classifying data, introduces dynamic sampling neural networks,investigates multiclass problem...

In multi-instance learning, each learning object consists of many descriptive instances. In the corresponding classification problems, each training object is labeled, but its constituent instances are not. The classification objective is to predict the class label of unseen objects. As in traditional single-instance classification, when the class sizes of multi-instance data are imbalanced, cl...

This paper proposes a novel method for multiview object pose classification through sequential learning and sensor fusion. The basic idea is to use images observed in visual and infrared (IR) bands, with the same sampling weight under a multi-class boosting framework. The main contribution of this paper is a multi-class AdaBoost classification framework where visual and infrared information int...

Classification of multi class images is very enviable for different recognition. This is affected by many factors such as noise, blur, low illumination, complex background, occlusion etc. Noise is one of the major factors causing degradation of the classification performance. This paper proposes an efficient method for classification of multi class object images which are corrupted by Gaussian ...

This paper is devoted to the multi-topic (multilabel) text classification problem. We propose two methods for reduction from ranking to the multi-label case. Unlike existing multi-label classification methods based on reduction from ranking problem, where the complex classification (threshold) function is being defined on the input feature space, in our approach we propose the construction of s...

This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer...

A common way to address a multi-class classification problem is to design a model that consists of hand picked binary classifiers and to combine them so as to solve the problem. Error such framework that deals with multi-class classification problems. Recent works in the ECOC domain has shown promising results demonstrating improved performance. Therefore, ECOC framework is a powerful tool to d...

We propose a novel local image descriptor called the Extended Multi-resolution Local Patterns, and a discriminative probabilistic framework for learning its parameters together with a multi-class image classifier. Our approach uses training data with image-level labels to learn the features which are discriminative for multi-class colonoscopy image classification. Experiments on a three class (...