Effective rough set feature selection via core-generating approximate minimum entropy discretization

Knowledge discovery in databases (KDD) concerns extraciing useful knowledge from a large amount of data stored in databases. Data mining is a crucial step of KDD and concerns mining useful patterns from such data. An important task of data mining is data classification which concerns learning a classifier from the data and using it to classify data with unknown classes. Data is described by features. However, for data with many features, the performance of the classifier can be low because the data contams redundant features which do not contain useful information for classification Feature selection concerns selecting the significant features from all the features of the data and can be used to improve classifier performances. Rough set theory (RST) is a mathematical method for approximating imprecise concepts of data Rough set feature selection aproach using RST. The feature subsets selected by RSFS are named reducts. RSFS works on data containing discrete features only. For data containing continuous features, discretization is needed to transform continuous features to discrete ones and works as a pre-processor of RSFS.EThOS - Electronic Theses Online ServiceGBUnited Kingdo