A dataset is said to be imbalanced when its classes are disproportionately represented in terms of the number of instances they contain. This problem is common in applications such as medical diagnosis of rare diseases, detection of fraudulent calls, signature recognition. In this paper we propose an alternative method for imbalanced learning, which balances the dataset using an undersampling s...

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on performance of algorithms. This paper provides detailed critical discussion objective evaluation context imbalanced data its classification accuracy. First, we present thorough experimental comparison imbalance. Unlike pr...

Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires high accuracy, the performance classifiers is crucial. However, datasets can lead to poor classification and conventional techniques, such as synthetic minority oversampling technique. As result, this study proposed balance betwee...

Learning from complex data with imbalanced label distribution is a challenging problem, especially when the data/label form structure, such as linearchain or tree-like. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced structured data. To model the complex relationship between the data and the structured label, we present...

Imbalanced learning data often emerges during the process of the knowledge discovery in data and presents a significant challenge for data mining methods. In this paper we investigate the influence of class imbalanced data on: artificial intelligence methods i.e. neural networks and support vector machine and on classical classification methods represented by RIPPER and Naïve Bayes classifier. ...

Today’s most of the research interest is in the application of evolutionary algorithms. One of the examples is classification rules in imbalanced domains. The problem of Imbalanced data sets plays a major challenge in data mining community. In imbalanced data sets, the number of instances of one class is much higher than the others, and the class of fewer representatives is of more interest fro...