Title :
Learning to predict: INC2.5
Author :
Hadzikadic, Mirsad ; Bohren, Benjamin F.
Author_Institution :
Dept. of Orthopaedic Inf. Res., North Carolina Univ., Charlotte, NC, USA
Abstract :
Discusses INC2.5, an incremental concept formation system. The goal of INC2.5 is to form a hierarchy of concept descriptions based on previously-seen instances which are to be used to predict the classification of a new instance description. Each subtree of the hierarchy consists of instances which are similar to each other. The further from the root, the greater the similarity is between the instances within the same groupings. The ability to classify instances based on their description has many applications. For example, in the medical field, doctors are required daily to diagnose patients, in other words to classify patients according to their symptoms. INC2.5 has been successfully applied to several domains, including breast cancer, general trauma, congressional voting records and the monk´s problems
Keywords :
heuristic programming; knowledge acquisition; learning by example; medical diagnostic computing; pattern classification; prediction theory; tree data structures; INC2.5; breast cancer; classification prediction learning; concept description hierarchy; congressional voting records; database mining; general trauma; incremental concept formation system; knowledge acquisition; monk´s problems; new instance description; patient classification; patient diagnosis; patient symptoms; previously-seen instances; similar instances; similarity-based learning; subtrees; Breast cancer; Classification tree analysis; Computational modeling; Computer simulation; Diseases; Humans; Knowledge acquisition; Medical diagnostic imaging; Spatial databases; Voting;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
