In this study, Artificial Bee Colony (abc) algorithm based classifier is used. Also, in order to improve the effectiveness of abcalgorithm, some modifications are done. New method is called Mabcalgorithm. Both metho...
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ISBN:
(纸本)9781479975723
In this study, Artificial Bee Colony (abc) algorithm based classifier is used. Also, in order to improve the effectiveness of abcalgorithm, some modifications are done. New method is called Mabcalgorithm. 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 abcalgorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, Mabcalgorithm based classifier provides higher classification success rates versus abcalgorithm, independent from Limit and MCN values.
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