Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through oversampling at the borderline b...

Breast cancer is non-skin that caused by several factors, including glandular ducts, cells, and breast support tissue, except for the skin of breast. if not treated immediately will be fatal sufferer, so early detection important patient's safety. The success depends on right diagnosis. Measurement accuracy a diagnosis can assisted statistical methods, namely classification. K-Nearest Neighbor ...

Abstract Researchers publish various studies to improve the performance of network intrusion detection systems. However, there is still a high false alarm rate and missing intrusions due class imbalance in multi-class dataset. This imbalanced distribution classes results low accuracy for minority classes. paper proposes Synthetic Multi-minority Oversampling (SMMO) framework by integrating with ...

Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction students’ academic achievement) based on predictive models. However, class imbalance problem educational datasets could hamper accuracy models as many these are designed assumption that predicted balanced. Although previous studies proposed several methods to...

Abstract Considering the complexities and challenges in classification of multiclass imbalanced fault conditions, this study explores systematic combination unsupervised supervised learning by hybridising clustering (CLUST) optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset f...

We show that the HyperPlay technique, which maintains a bag of updatable models for sampling an imperfect-information game, is more efficient than taking random samples of play sequences. Also, we demonstrate that random sampling may become impossible under the practical constraints of a game. We show the HyperPlay sample can become biased and not uniformly distributed across an information set...

The paper discusses problems of constructing classifiers from imbalanced data. Re-sampling approaches that change the original class distribution are often used to improve performance of classifiers for the minority class. We describe a new approach to selective pre-processing of imbalanced data which combines local over-sampling of the minority class with filtering difficult examples from majo...