Imbalanced classification is a challenging task in the fields of machine learning and data mining. Cost-sensitive can tackle this issue by considering different misclassification costs classes. Weighted extreme (W-ELM) takes cost-sensitive strategy to alleviate bias towards majority class achieve better performance. However, W-ELM may not optimal weights for samples from classes due adoption em...

Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effe...

In this paper, we propose a novel framework to deal with data imbalance in class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This framework is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processe...

Many real world data mining applications involve learning from imbalanced data sets. Learning from data sets that contain very few instances of the minority (or interesting) class usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is spe...

The application of deep neural networks to address automatic modulation recognition (AMR) challenges has gained increasing popularity. Despite the outstanding capability learning in feature extraction, predictions based on low-data regimes with imbalanced classes signals generally result low accuracy due an insufficient number training examples, which hinders wide adoption methods practical app...

A key challenge of oversampling in imbalanced classification is that the generation new minority samples often neglects usage majority classes, resulting most sampling spreading whole space. In view this, we present a framework based on counterfactual theory. Our introduces objective by leveraging rich inherent information classes and explicitly perturbing to generate territory It can be analyt...

Associative Classification (AC) is a well known tool in knowledge discovery and it has been proved to extract competitive classifiers. However, imbalanced data has posed a challenge for most classifier learn ing algorithms including AC methods. Because in the AC process, Interestingness Measure (IM) p lays an important role to generate interesting rules and build good classifiers, it is very im...

Several works point out class imbalance as an obstacle on applying machine learning algorithms to real world domains. However, in some cases, learning algorithms perform well on several imbalanced domains. Thus, it does not seem fair to directly correlate class imbalance to the loss of performance of learning algorithms. In this work, we develop a systematic study aiming to question whether cla...

With the advent of information technology, amount online data generation has been massive. Recommendation systems have become an effective tool in filtering and solving problem overload. Machine learning algorithms to build these recommendation require well-balanced terms class distribution, but real-world datasets are mostly imbalanced nature. Imbalanced imposes a classifier focus more on majo...