Abstract This paper presents twin-hyperspheres of resisting noise for binary classification to imbalanced data with noise. First, employing the decision evaluating contributions created by points training hyperspheres, then label density estimator is introduced into fuzzy membership quantize provided contributions, and finally, unknown can be assigned corresponding classes. Utilizing decision, ...

We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn accurate “fewshot” models for classes in the tail of the class distribution, for which little data is available. We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to...

Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. This problem may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. The majority of the studies in this area are oriented, mainly, to resolve problems with two classes. How...

Transfer learning has benefited many real-world applications where labeled data are abundant in source domains but scarce in the target domain. As there are usually multiple relevant domains where knowledge can be transferred, multiple source transfer learning (MSTL) has recently attracted much attention. However, we are facing two major challenges when applying MSTL. First, without knowledge a...

credit scoring is a classification problem leading to introducing numerous techniques to deal with it such as support vector machines, neural networks and rule-based classifiers. rule bases are the top priority in credit decision making because of their ability to explicitly distinguish between good and bad applicants.in a credit- scoring context, imbalanced data sets frequently occur as the nu...

This paper demonstrates that the imbalanced data sets have a negative effect on the performance of LDA theoretically. This theoretical analysis is confirmed by the experimental results: using several sampling methods to rebalance the imbalanced data sets, it is found that the performances of LDA on balanced data sets are superior to those of LDA on imbalanced data sets. 2006 Pattern Recognition...

An ensemble of classifiers consists of a set of individually trained classifiers whose predictions are combined when classifying new instances. The resulting ensemble is generally more accurate than the individual classifiers it consists of. In particular, one of the most popular ensemble methods, the Boosting approach, improves the predictive performance of weak classifiers, which can achieve ...

The collected data from industrial machines are often imbalanced, which poses a negative effect on learning algorithms. However, this problem becomes more challenging for mixed type of or while there is overlapping between classes. Class- imbalance requires robust system can timely predict and classify the data. We propose new adversarial network simultaneous classification fault detection. In ...

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in malignant and benign patient classes significantly different. Over-sampling techniques can be used to re-balance construct more effective models. Moreover, some related studies have considered feature selection remove irrelevant features from for further performance improvement. However, since or...

The class imbalance problem in classification has been recognized as a significant research problem in recent years and a number of methods have been introduced to improve classification results. Rebalancing class distributions (such as over-sampling or under-sampling of learning datasets) has been popular due to its ease of implementation and relatively good performance. For the Support Vector...