Performance analysis of ABCMiner algorithm with different objective functions

Author

Koylu, F. ; Celik, M. ; Karaboga, D.

Author_Institution

Bilgisayar Muhendisligi Bolumu, Erciyes Univ., Kayseri, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

5

Abstract

Metaheuristic-based data mining algorithms are frequently used in literature for discovering meaningful rules out of huge datasets. However, in the design criteria of these algorithms, the choice of objective functions affects the performance of the algorithm and classification accuracy. ABCMiner is one of these algorithms and is a classification rule learning algorithm based on a swarm based metaheuristic algorithm, Artificial Bee Colony algorithm. In this paper, the performances of two different objective functions on ABCMiner are evaluated. The experimental evaluation is conducted using real datasets.

Keywords

data mining; learning (artificial intelligence); particle swarm optimisation; pattern classification; ABCMiner algorithm; artificial bee colony algorithm; classification accuracy; classification rule learning algorithm; design criteria; huge datasets; metaheuristic-based data mining algorithms; objective functions; performance analysis; real datasets; swarm based metaheuristic algorithm; Algorithm design and analysis; Art; Breast; Classification algorithms; Data mining; Genetic algorithms; Machine learning algorithms; ABCMiner; Artificial Bee Colony Algorithm; Classification; Data Mining; Rule Learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531402

Filename

6531402