An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset
Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier bu...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/821798 |
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| author | Senthilkumar Devaraj S. Paulraj |
| author_facet | Senthilkumar Devaraj S. Paulraj |
| author_sort | Senthilkumar Devaraj |
| collection | DOAJ |
| description | Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier built from the MDD are typically more expensive or time-consuming. Therefore, we need a robust feature selection technique for selecting the optimum single subset of the features of the MDD for further analysis or to design a classifier. In this paper, an efficient feature selection algorithm is proposed for the classification of MDD. The proposed multidimensional feature subset selection (MFSS) algorithm yields a unique feature subset for further analysis or to build a classifier and there is a computational advantage on MDD compared with the existing feature selection algorithms. The proposed work is applied to benchmark multidimensional datasets. The number of features was reduced to 3% minimum and 30% maximum by using the proposed MFSS. In conclusion, the study results show that MFSS is an efficient feature selection algorithm without affecting the classification accuracy even for the reduced number of features. Also the proposed MFSS algorithm is suitable for both problem transformation and algorithm adaptation and it has great potentials in those applications generating multidimensional datasets. |
| format | Article |
| id | doaj-art-b5b98b28a4ae4983b3a988a99c4e548a |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-b5b98b28a4ae4983b3a988a99c4e548a2025-02-03T07:25:17ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/821798821798An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional DatasetSenthilkumar Devaraj0S. Paulraj1Department of Computer Science and Engineering, University College of Engineering, Anna University, Tiruchirappalli, Tamil Nadu, IndiaDepartment of Mathematics, College of Engineering, Anna University, Tamil Nadu, IndiaMultidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier built from the MDD are typically more expensive or time-consuming. Therefore, we need a robust feature selection technique for selecting the optimum single subset of the features of the MDD for further analysis or to design a classifier. In this paper, an efficient feature selection algorithm is proposed for the classification of MDD. The proposed multidimensional feature subset selection (MFSS) algorithm yields a unique feature subset for further analysis or to build a classifier and there is a computational advantage on MDD compared with the existing feature selection algorithms. The proposed work is applied to benchmark multidimensional datasets. The number of features was reduced to 3% minimum and 30% maximum by using the proposed MFSS. In conclusion, the study results show that MFSS is an efficient feature selection algorithm without affecting the classification accuracy even for the reduced number of features. Also the proposed MFSS algorithm is suitable for both problem transformation and algorithm adaptation and it has great potentials in those applications generating multidimensional datasets.http://dx.doi.org/10.1155/2015/821798 |
| spellingShingle | Senthilkumar Devaraj S. Paulraj An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset The Scientific World Journal |
| title | An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset |
| title_full | An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset |
| title_fullStr | An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset |
| title_full_unstemmed | An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset |
| title_short | An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset |
| title_sort | efficient feature subset selection algorithm for classification of multidimensional dataset |
| url | http://dx.doi.org/10.1155/2015/821798 |
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