Recently, Class imbalance problems have growing interest because of their classification difficulty caused by the imbalanced class distributions. In particular, many ensemble learning and machine learning methods have been proposed for classification of imbalance problem. However, these methods producing poor predictive accuracy of classification for two-class imbalanced dataset. In this paper,...

With the continuous expansion of data availability in many large-scale, complex, and networked systems, it becomes critical to advance raw data from fundamental research on the Big Data challenge to support decision-making processes. Although existing machine-learning and data-mining techniques have shown great success in many real-world applications, learning from imbalanced data is a relative...

The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and ‘robust’ fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during training is often not appropriate for d...

Abstract Online supervised learning from fast-evolving data streams, particularly in domains such as health, the environment, and manufacturing, is a crucial research area. However, these often experience class imbalance, which can skew distributions. It essential for online algorithms to analyze large datasets real-time while accurately modeling rare or infrequent classes that may appear burst...

Learning from imbalanced data sets is one of the challenging problems in machine learning, which means the number of negative examples is far more than that of positive examples. The main problems of existing methods are: (1) The degree of re-sampling, a key factor greatly affecting performance, needs to be pre-fixed, which is difficult to make the optimal choice; (2) Many useful negative sampl...

Many real-world domains present the problem of imbalanced data sets, where examples of one classes significantly outnumber examples of other classes. This makes learning difficult, as learning algorithms based on optimizing accuracy over all training examples will tend to classify all examples as belonging to the majority class. We introduce a method to deal with this problem by means of creati...

In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison with the class of non-defaulters is a very common situation, but it has still received little attention. The present paper investigates the suitability and performance of several resampling techniques when applied in conjunction with statistical and artificial intelligence pred...

A major challenge for building statistical models in the big data era is that the available data volume may exceed the computational capability. A common approach to solve this problem is to employ a subsampled dataset that can be handled by the available computational resources. In this paper, we propose a general subsampling scheme for large-scale multi-class logistic regression, and examine ...