Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification
This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/419187 |
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| _version_ | 1832551582043996160 |
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| author | Mustafa Serter Uzer Nihat Yilmaz Onur Inan |
| author_facet | Mustafa Serter Uzer Nihat Yilmaz Onur Inan |
| author_sort | Mustafa Serter Uzer |
| collection | DOAJ |
| description | This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications. |
| format | Article |
| id | doaj-art-0d59b4a946bc4be980926b3e210c39ec |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-0d59b4a946bc4be980926b3e210c39ec2025-02-03T06:01:08ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/419187419187Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets ClassificationMustafa Serter Uzer0Nihat Yilmaz1Onur Inan2Electrical-Electronics Engineering, Faculty of Engineering, Selcuk University, Konya, TurkeyElectrical-Electronics Engineering, Faculty of Engineering, Selcuk University, Konya, TurkeyComputer Engineering, Faculty of Engineering, Selcuk University, Konya, TurkeyThis paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.http://dx.doi.org/10.1155/2013/419187 |
| spellingShingle | Mustafa Serter Uzer Nihat Yilmaz Onur Inan Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification The Scientific World Journal |
| title | Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification |
| title_full | Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification |
| title_fullStr | Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification |
| title_full_unstemmed | Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification |
| title_short | Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification |
| title_sort | feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification |
| url | http://dx.doi.org/10.1155/2013/419187 |
| work_keys_str_mv | AT mustafaserteruzer featureselectionmethodbasedonartificialbeecolonyalgorithmandsupportvectormachinesformedicaldatasetsclassification AT nihatyilmaz featureselectionmethodbasedonartificialbeecolonyalgorithmandsupportvectormachinesformedicaldatasetsclassification AT onurinan featureselectionmethodbasedonartificialbeecolonyalgorithmandsupportvectormachinesformedicaldatasetsclassification |