Imbalanced class distribution affects many applications in machine learning, including medical diagnostics, text classification, intrusion detection and others. In this paper, we propose a novel ensemble classification method designed to deal with imbalanced data. The proposed trains each tree the using uniquely generated synthetically balanced data balancing is carried out via kernel density e...

Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance....

In many real-world applications, the problem of learning from imbalanced data (the imbalanced learningproblem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent...

Some real world data mining applications present imbalanced or skewed class distributions. In these domains, the underrepresented classes are often the ones we are more interested in. However, most learning algorithms are not able to induce meaningful classifiers in some imbalanced domains. One reason for this poor performance is that learning algorithms tend to focus in abundant classes to max...

Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been reported to yield promising results. However, the majority of ensemble metho...

BACKGROUND In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in ...

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...

In this contribution we carry out an analysis of the Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of balanced and imbalanced data-sets with different degrees of imbalance. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best type of Fuzzy Reasoning Method in each case, also studying the cooperation of some pre-pro...

A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs...

Imbalanced data sets are becoming ubiquitous, as many applications have very few instances of the “interesting” or “abnormal” class. Traditional machine learning algorithms can be biased towards majority class due to over-prevalence. It is desired that the interesting (minority) class prediction be improved, even if at the cost of additional majority class errors. In this paper, we study three ...