DocumentCode :
250381
Title :
Using ABC algorithm for classification and analysis on effects of control parameters
Author :
Dilmac, Selim ; Korurek, Mehmet
Author_Institution :
Fen Bilimleri Enstitusu, Istanbul Teknik Univ., Istanbul, Turkey
fYear :
2014
fDate :
16-17 Oct. 2014
Firstpage :
1
Lastpage :
4
Abstract :
In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.
Keywords :
classification; data analysis; electrocardiography; feature extraction; feature selection; learning (artificial intelligence); medical information systems; medical signal processing; signal classification; swarm intelligence; ABC algorithm effectiveness; BUPA; IRIS; MABC algorithm based classifier; MCN parameter; MITBIH ECG database; PIMA; SCTR control parameter; UCI Machine Learning Repository; WINE; analysis algorithm; artificial bee colony algorithm based classifier; class overlap; classification algorithm; classification success rate; control parameter effect; feature effect; feature selection; limit parameter; multidimensional dataset classification; validity index; Classification algorithms; Educational institutions; Electrocardiography; Indexes; Iris; Machine learning algorithms; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering Meeting (BIYOMUT), 2014 18th National
Conference_Location :
Istanbul
Type :
conf
DOI :
10.1109/BIYOMUT.2014.7026368
Filename :
7026368
Link To Document :
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