Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2015
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| _version_ | 1876482457460539392 |
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| author | A. Khan |
| author_facet | A. Khan |
| contents | Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm A. Khan A. R. Baig Ingeniería Optimization classification genetic algorithm Feature subset selection This paper presents an evolutionary al gorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large num ber of features called attribut es. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a str ong requirement to select a s ubset of the f eatures before building the classifier. This proposed technique treats featur e subset selection as multi-ob jective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA-II). The fitness value of a particular feature subset is measured by using ID 3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from t he UCI machine repository. Th e experiments demonstrate the feasibility of using NSGA-II fo r feature subset selection. 2015 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47436895014 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.13 |
| format | Artículo científico |
| id | redalyc_47436895014 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm A. Khan Ingeniería Optimization classification genetic algorithm Feature subset selection Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm A. Khan A. R. Baig Ingeniería Optimization classification genetic algorithm Feature subset selection This paper presents an evolutionary al gorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large num ber of features called attribut es. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a str ong requirement to select a s ubset of the f eatures before building the classifier. This proposed technique treats featur e subset selection as multi-ob jective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA-II). The fitness value of a particular feature subset is measured by using ID 3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from t he UCI machine repository. Th e experiments demonstrate the feasibility of using NSGA-II fo r feature subset selection. 2015 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47436895014 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.13 |
| title | Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm |
| topic | Ingeniería Optimization classification genetic algorithm Feature subset selection |
| url | https://www.redalyc.org/articulo.oa?id=47436895014 |