Naslov
Cost-sensitive feature reduction applied to a hybrid genetic algorithm

Autori
Lavrač, Nada ; Gamberger, Dragan ; Turney, Peter

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
7th International Workshop on Algorithmic Learning Theory ALT-96 / Arikawa, Setsuo ; Sharma, Arun K. - Berlin : Springer, 1996, 127-134

Skup
7th International Workshop ALT '96

Mjesto i datum
Sydney, Australija, 23.10.1996. - 25.10.1996

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
machine learning; relevant literals; genetic algorithm

Sažetak
Inductive concept learning is concerned with the induction of hypotheses from training data, given appropriate background knowledge about the learning problem. This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA