Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we pr...

Imbalanced learning, or learning from imbalanced data, is a challenging problem in both academy and industry. Nowadays, the streaming imbalanced data become popular and trigger the volume, velocity, and variety issues of learning from these data. To tackle these issues, online learning algorithms are proposed to learn a linear classifier via maximizing the AUC score. However, the developed line...

Logistic regression is a widely used method in several fields. When applying logistic to imbalanced data, wherein the majority classes dominate minority classes, all class labels are estimated as “majority class.” In this study, we use an F-measure optimization improve performance of applied data. Although many methods adopt ratio estimators approximate F-measure, tends exhibit more bias than w...

We investigate the beating of signal and idler waves, which have imbalanced signal to noise ratios, in a phase-sensitive parametric amplifier. Imbalanced signal to noise ratios are achieved in two ways; first by imbalanced noise loading; second by varying idler to signal input power ratio. In the case of imbalanced noise loading the phase-sensitive amplifier improved the signal to noise ratio f...

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...

Classification is one of the most important research contents in data mining and traditional classification methods are relatively mature, when dealing with well-balanced data they can make good performances. But in real world the data is usually imbalanced, that is, most of the data are in majority class and little data are in minority class. Imbalanced data set cause the deduction of the prec...

Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is use resampling, which adjusts ratio between different classes, making more balanced. This research looks at resampli...

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance recent years. When class distribution imbalanced, classical machine learning algorithms tend to move strongly towards majority disregard minority. Therefore, accuracy may be high, but model cannot recognize instances minority classify them, leading many...

Most classifiers work well when the class distribution in the response variable of the dataset is well balanced. Problems arise when the dataset is imbalanced. This paper applied four methods: Oversampling, Undersampling, Bagging and Boosting in handling imbalanced datasets. The cardiac surgery dataset has a binary response variable (1=Died, 0=Alive). The sample size is 4976 cases with 4.2% (Di...

Training deep learning models on medical datasets that perform well for all classes is a challenging task. It often the case suboptimal performance obtained some due to natural class imbalance issue comes with data. An effective way tackle this problem by using targeted active learning, where we iteratively add data points belong rare classes, training However, existing methods are ineffective ...