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...

With the recent growth of Internet, volume data has also increased. In particular, increase in amount unstructured makes it difficult to manage data. Classification is needed order be able use for various purposes. Since manually classify ever-increasing purpose types analysis and evaluation, automatic classification methods are needed. addition, performance imbalanced multi-class a challenging...