Decision Tree based Classifiers for Large Datasets
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2013
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| _version_ | 1876438367087886336 |
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| author | Anilu Franco-Arcega |
| author_facet | Anilu Franco-Arcega |
| contents | Decision Tree based Classifiers for Large Datasets Anilu Franco-Arcega Jesús Ariel Carrasco-Ochoa Guillermo Sánchez-Díaz José Francisco Martínez-Trinidad Computación Decision trees large datasets supervised classification In this paper, several algorithms have been developed for building decision trees from large data ets. These algorithms overcome some restrictions of the most recent algorithms in the state of the art. Three of these algorithms have been designed to process datasets described exclusively by numeric attributes, and the fourth one, for processing mixed datasets. The proposed algorithms process all the training instances without storing the whole dataset in the main memory. Besides, the developed algorithms are faster than the most recent algorithms for building decision trees from large datasets, and reach competitive accuracy rates. 2013 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61525677010 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.17 |
| format | Artículo científico |
| id | redalyc_61525677010 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Decision Tree based Classifiers for Large Datasets Anilu Franco-Arcega Computación Decision trees large datasets supervised classification Decision Tree based Classifiers for Large Datasets Anilu Franco-Arcega Jesús Ariel Carrasco-Ochoa Guillermo Sánchez-Díaz José Francisco Martínez-Trinidad Computación Decision trees large datasets supervised classification In this paper, several algorithms have been developed for building decision trees from large data ets. These algorithms overcome some restrictions of the most recent algorithms in the state of the art. Three of these algorithms have been designed to process datasets described exclusively by numeric attributes, and the fourth one, for processing mixed datasets. The proposed algorithms process all the training instances without storing the whole dataset in the main memory. Besides, the developed algorithms are faster than the most recent algorithms for building decision trees from large datasets, and reach competitive accuracy rates. 2013 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61525677010 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.17 |
| title | Decision Tree based Classifiers for Large Datasets |
| topic | Computación Decision trees large datasets supervised classification |
| url | https://www.redalyc.org/articulo.oa?id=61525677010 |