A Supervised Discretization Method for Quantitative and Qualitative Ordered Variables
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| Format: | Artículo científico |
| Langue: | en |
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Instituto Politécnico Nacional
2006
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| _version_ | 1876457314934849536 |
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| author | Francisco J. Ruiz |
| author_facet | Francisco J. Ruiz |
| contents | A Supervised Discretization Method for Quantitative and Qualitative Ordered Variables Francisco J. Ruiz Cecilio Angulo Núria Agell Computación Regression Intervalar distance Qualitative Reasoning Supervised Discretization In this work, a new technique to define cut-points in the discretization process of a continuous attribute is presented. This method is used as a prior step in a regression problem, considered as a learning problem in which the output variable can be either quantitative (continuous or discreet) or qualitative defined over an ordinal scale. The proposed method emphasizes the concept of location to determine discretization cut-points. In the case of continuous outputs, the method is based on the maximization of the difference between distributions by using intervalar distances. In the case of qualitative outputs, a qualitative distance is defined over a structure of absolute orders of magnitude. The main characteristics of the method presented are illustrated through three examples, two for continuous outputs and the last for a qualitative output. 2006 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61590403 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.9 |
| format | Artículo científico |
| id | redalyc_61590403 |
| institution | Redalyc |
| language | en |
| publishDate | 2006 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | A Supervised Discretization Method for Quantitative and Qualitative Ordered Variables Francisco J. Ruiz Computación Regression Intervalar distance Qualitative Reasoning Supervised Discretization A Supervised Discretization Method for Quantitative and Qualitative Ordered Variables Francisco J. Ruiz Cecilio Angulo Núria Agell Computación Regression Intervalar distance Qualitative Reasoning Supervised Discretization In this work, a new technique to define cut-points in the discretization process of a continuous attribute is presented. This method is used as a prior step in a regression problem, considered as a learning problem in which the output variable can be either quantitative (continuous or discreet) or qualitative defined over an ordinal scale. The proposed method emphasizes the concept of location to determine discretization cut-points. In the case of continuous outputs, the method is based on the maximization of the difference between distributions by using intervalar distances. In the case of qualitative outputs, a qualitative distance is defined over a structure of absolute orders of magnitude. The main characteristics of the method presented are illustrated through three examples, two for continuous outputs and the last for a qualitative output. 2006 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61590403 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.9 |
| title | A Supervised Discretization Method for Quantitative and Qualitative Ordered Variables |
| topic | Computación Regression Intervalar distance Qualitative Reasoning Supervised Discretization |
| url | https://www.redalyc.org/articulo.oa?id=61590403 |