While transmitting the data over communication channel, various problems such as communication error and mismatch arises. For reducing those errors, frequency selective I/Q imbalance using Direct Conversion transmitters can be implemented. I/Q imbalance compensation procedure has been derived and incoherent measurements are required for avoiding the need for time, frequency and phase synchroniz...

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexis...

In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples...

BACKGROUND Although data from longitudinal studies are sparse, effort-reward imbalance (ERI) seems to affect work ability. However, the potential pathway from restricted work ability to ERI must also be considered. Therefore, the aim of our study was to analyse cross-sectional and longitudinal associations between ERI and work ability and vice versa. METHODS Data come from the Second German S...

Speech keyword spotting system is a critical component of human-computer interfaces. And connectionist temporal classifier (CTC) has been proven to be an effective tool for that task. However, the standard training process speech faces data imbalance issue where positive samples are usually far less than negative samples. Numerous easy-training examples overwhelm training, resulting in degenera...

Class imbalance has been reported as an important obstacle to apply traditional learning algorithms to real-world domains. Recent investigations have questioned whether the imbalance is the unique factor that hinders the performance of classifiers. In this paper, we study the behavior of six algorithms when classifying imbalanced, overlapped data sets under uncommon situations (e.g., when the o...

This article investigates whether gender imbalance may be conducive to domestic terrorism in developing countries. A female-dominated society may not provide sufficient administration, law, or order to limit domestic terrorism, especially since societies in developing countries primarily turn to males for administration, policing, and paramilitary forces. Other economic considerations support f...

In classification or prediction tasks, data imbalance problem is frequently observed when most of instances belong to one majority class. Data imbalance problem has received considerable attention in machine learning community because it is one of the main causes that degrade the performance of classifiers or predictors. In this paper, we propose geometric mean based boosting algorithm (GMBoost...

Streaming data is pervasive in a multitude of data mining applications. One fundamental problem in the task of mining streaming data is distributional drift over time. Streams may also exhibit high and varying degrees of class imbalance, which can further complicate the task. In scenarios like these, class imbalance is particularly difficult to overcome and has not been as thoroughly studied. I...