In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes havi...

Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exist many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in ...

Most existing classification approaches assume the underlying training set is evenly distributed. In class imbalanced classification, the training set for one class (majority) far surpassed the training set of the other class (minority), in which, the minority class is often the more interesting class. In this paper, we review the issues that come with learning from imbalanced class data sets a...

1 Introduction The class imbalance problem is a challenge to machine learning and data mining, and it has attracted significant research recent years. A classifier affected by the class imbalance problem for a specific data set would see strong accuracy overall but very poor performance on the minority class. The imbalance data sets are pervasive in real-world applications. Examples of these ki...

Class imbalance is one of the challenges of machine learning and data mining fields. Imbalance data sets degrades the performance of data mining and machine learning techniques as the overall accuracy and decision making be biased to the majority class, which lead to misclassifying the minority class samples or furthermore treated them as noise. This paper proposes a general survey for class im...

Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. Moreover, in this task usually there is no similarity between the distribution of training and...