Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs
In this paper we propose a method for scaling up filterbased feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculat...
| Main Authors: | , , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/4916 |
| _version_ | 1789406643980599296 |
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| author | Salmerón Cerdán, Antonio Madsen, Anders L. Jensen, Frank Langseth, Helge Nielsen, Thomas D. Ramos López, Darío Martínez, Ana M. Masegosa, Andrés R. |
| author_facet | Salmerón Cerdán, Antonio Madsen, Anders L. Jensen, Frank Langseth, Helge Nielsen, Thomas D. Ramos López, Darío Martínez, Ana M. Masegosa, Andrés R. |
| author_sort | Salmerón Cerdán, Antonio |
| collection | DSpace |
| description | In this paper we propose a method for scaling up filterbased feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required
statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculations between the available computing units is determined based on balanced incomplete
block designs, a strategy first developed within the area of statistical design of experiments. We show the scalability of our method through a series of experiments on synthetic and real-world datasets. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-4916 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2017 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-49162023-04-12T19:39:03Z Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs Salmerón Cerdán, Antonio Madsen, Anders L. Jensen, Frank Langseth, Helge Nielsen, Thomas D. Ramos López, Darío Martínez, Ana M. Masegosa, Andrés R. Bandpass filters Design of experiments Balanced incomplete block design Computing units Conditional mutual information Filter-based Real-world datasets Scaling-up Statistical design of experiments In this paper we propose a method for scaling up filterbased feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculations between the available computing units is determined based on balanced incomplete block designs, a strategy first developed within the area of statistical design of experiments. We show the scalability of our method through a series of experiments on synthetic and real-world datasets. 2017-07-17T10:48:34Z 2017-07-17T10:48:34Z 2016 info:eu-repo/semantics/article The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-672-9-743 http://hdl.handle.net/10835/4916 10.3233/978-1-61499-672-9-743 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| spellingShingle | Bandpass filters Design of experiments Balanced incomplete block design Computing units Conditional mutual information Filter-based Real-world datasets Scaling-up Statistical design of experiments Salmerón Cerdán, Antonio Madsen, Anders L. Jensen, Frank Langseth, Helge Nielsen, Thomas D. Ramos López, Darío Martínez, Ana M. Masegosa, Andrés R. Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs |
| title | Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs |
| title_full | Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs |
| title_fullStr | Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs |
| title_full_unstemmed | Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs |
| title_short | Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs |
| title_sort | parallel filter-based feature selection based on balanced incomplete block designs |
| topic | Bandpass filters Design of experiments Balanced incomplete block design Computing units Conditional mutual information Filter-based Real-world datasets Scaling-up Statistical design of experiments |
| url | http://hdl.handle.net/10835/4916 |
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