The objective of this paper is to minimize the number of samples required for training algorithms involving support vectors while maximizing knowledge of the target class. A method is proposed which uses autoencoder in conjunction with farthest boundary point extraction for selecting most...

Associative classifiers are new classification approach that use association rules for classification. An important advantage of these classification systems is that, using association rule mining (ARM) they are able to examine several features at a time. Many applications can benefit from good...