The term “data imbalance” in classification is a well established phenomenon in which data set contains unbalanced class distributions. Dataset is called unbalanced if it contains at least one class which is presented by very few examples. A range of solutions have been proposed for the problem of data imbalance including data sampling, cost evaluation of model, bagging, boosting, Genetic Progr...

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it’s promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behin...

WiMAX systems are very popular in wireless communication now-a-days. The main reason behind its popularity is high data rates possible in WiMAX systems. But due to in-phase quadrature imbalance (i.e. IQ imbalance) at the transmitter (Tx) and receiver (Rx), the system performance may be degraded in WiMAX systems. Therefore, it is necessary to compensate for the in-phase quadrature imbalance. In ...

Class imbalance problem are raised when one class having maximum number of examples than other classes. The classical classifiers of balance datasets cannot deal with the class imbalance problem because they pay more attention to the majority class. The main drawback associated with it majority class is loss of important information. The Class imbalance problem is a difficult due to the amount ...

Practitioners of data mining and machine learning have long observed that the imbalance of classes in a data set has a negative impact on the quality of classifiers trained on that data. Numerous techniques for coping with such imbalances have been proposed, but nearly all lack any theoretical grounding. By contrast, the standard theoretical analysis of machine learning admits no dependence on ...