So far few theoretical works investigated the conditions under which specific fusion rules can work well, and a unifying framework for comparing rules of different complexity is clearly beyond the state of the art. A clear theoretical comparison is lacking even if one focuses on specific classes of combiners (e.g., linear combiners). In this paper, we theoretically compare simple and weighted a...

We introduce an approach to learning from imbalanced class distributions that does not change the underlying data distribution. The ICC algorthm decomposes majority classes into smaller subclasses that create a more balanced class distribution. In this paper, we explain how ICC can not only address the class imbalance problem but may also increase the expressive power of the hypothesis space. W...

Breast cancer is one of the most dangerous carcinomas for middle-aged and older women in the world. Mammography is a detection tool that assists the radiologists in reading the mammograms. In this paper, new techniques are proposed to detect and classify the masses automatically. These techniques improve the detection and classification process. Classification of masses into benign or malignant...

This contribution faces the ToBI accent recognition problem with the goal of multiclass identification vs. the more conservative Accent vs. No Accent approach. A neural network and a decision tree are used for automatic recognition of the ToBI accents in the Boston Radio Corpus. Multiclass classification results show the difficulty of the problem and the impact of imbalanced classes. A study of...

Data mining has been widely adopted in recent years in many fields, especially in the medical field. This paper highlights the prediction of unknown primary tumors in the dataset. The multiclass classifier with Random forest is used for classification of multiclass dataset as it gives much higher accuracy than binary classifiers. SMOTE method for this imbalanced dataset with Randomize technique...

In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of noisy and borderline examples from the minority class on the classifier performance. Results showed that if data was sufficiently disturbed by these factors, then the focused re-sampling methods – NCR and our SPIDER2 ...

Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) archit...

Analysis of clinical records contributes to the Traditional Chinese Medicine (TCM) experience expansion and techniques promotion. More than two diagnostic classes (diagnostic syndromes) in the clinical records raise a popular data mining problem: multi-value classification. In this paper, we propose a novel multi-class classifier, named Multiple Asymmetric Partial Least Squares Classifier (MAPL...

Rail defect detection by video cameras has recently gained much attention in both academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning techniques. In this paper we investigate if positive defective candidates selected from the unlabeled data can...

Covid-19 has spread across the world and many different vaccines have been developed to counter its surge. To identify correct sentiments associated with from social media posts, this paper aims fine-tune pre-trained transformer models on tweets Covid vaccines, specifically RoBERTa, XLNet BERT which are recently introduced state-of-the-art bi-directional models, domain-specific BERTweet CT-BERT...