The complexity and accuracy of classification algorithms largely depend on the size quality feature set used to build classifiers. Feature evaluation selection are critical steps decide a small high-quality features accurate efficient classifiers since low-quality not only have negative impacts results but also increase algorithms. Current popular sufficient in selecting discarding features, es...

Concept drift and class imbalanced data are major challenging processes involved in modern streaming classification. Particularly, when integrated with difficult factors like the existence of noise, overlapping distribution, concept drift, imbalance can considerably affect classifier results. In addition, various challenges performance existing oversampling schemes such as SMOTE its derivatives...

How to extend standard support vector machines to solve multi-class classification problem and yield the outputs in the frame of Dempster-Shafer theory is useful. The multi-class probability support vector machine is proposed, firstly. The Dempster-Shafer theory based multi-class support vector machine is designed by constructing probability support vector machines for binary classification usi...