Effective Wrapper-Filter hybridization through GRASP Schemata

Mohamed Amir Esseghir
Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, PMLR 10:45-54, 2010.

Abstract

Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence techniques to feature selection problem requirements is one of the primary impediments. A new improved metaheuristic based on \textitGreedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Selection. Our devised optimization approach provides an effective scheme for wrapper-filter hybridization through the adaptation of GRASP components. The paper investigates, the GRASP component design as well as its adaptation to the feature selection problem. Carried out experiments showed Empirical effectiveness of the devised approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v10-esseghir10a, title = {Effective Wrapper-Filter hybridization through GRASP Schemata}, author = {Esseghir, Mohamed Amir}, booktitle = {Proceedings of the Fourth International Workshop on Feature Selection in Data Mining}, pages = {45--54}, year = {2010}, editor = {Liu, Huan and Motoda, Hiroshi and Setiono, Rudy and Zhao, Zheng}, volume = {10}, series = {Proceedings of Machine Learning Research}, address = {Hyderabad, India}, month = {21 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v10/esseghir10a/esseghir10a.pdf}, url = {https://proceedings.mlr.press/v10/esseghir10a.html}, abstract = {Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence techniques to feature selection problem requirements is one of the primary impediments. A new improved metaheuristic based on \textitGreedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Selection. Our devised optimization approach provides an effective scheme for wrapper-filter hybridization through the adaptation of GRASP components. The paper investigates, the GRASP component design as well as its adaptation to the feature selection problem. Carried out experiments showed Empirical effectiveness of the devised approach.} }
Endnote
%0 Conference Paper %T Effective Wrapper-Filter hybridization through GRASP Schemata %A Mohamed Amir Esseghir %B Proceedings of the Fourth International Workshop on Feature Selection in Data Mining %C Proceedings of Machine Learning Research %D 2010 %E Huan Liu %E Hiroshi Motoda %E Rudy Setiono %E Zheng Zhao %F pmlr-v10-esseghir10a %I PMLR %P 45--54 %U https://proceedings.mlr.press/v10/esseghir10a.html %V 10 %X Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence techniques to feature selection problem requirements is one of the primary impediments. A new improved metaheuristic based on \textitGreedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Selection. Our devised optimization approach provides an effective scheme for wrapper-filter hybridization through the adaptation of GRASP components. The paper investigates, the GRASP component design as well as its adaptation to the feature selection problem. Carried out experiments showed Empirical effectiveness of the devised approach.
RIS
TY - CPAPER TI - Effective Wrapper-Filter hybridization through GRASP Schemata AU - Mohamed Amir Esseghir BT - Proceedings of the Fourth International Workshop on Feature Selection in Data Mining DA - 2010/05/26 ED - Huan Liu ED - Hiroshi Motoda ED - Rudy Setiono ED - Zheng Zhao ID - pmlr-v10-esseghir10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 10 SP - 45 EP - 54 L1 - http://proceedings.mlr.press/v10/esseghir10a/esseghir10a.pdf UR - https://proceedings.mlr.press/v10/esseghir10a.html AB - Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence techniques to feature selection problem requirements is one of the primary impediments. A new improved metaheuristic based on \textitGreedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Selection. Our devised optimization approach provides an effective scheme for wrapper-filter hybridization through the adaptation of GRASP components. The paper investigates, the GRASP component design as well as its adaptation to the feature selection problem. Carried out experiments showed Empirical effectiveness of the devised approach. ER -
APA
Esseghir, M.A.. (2010). Effective Wrapper-Filter hybridization through GRASP Schemata. Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, in Proceedings of Machine Learning Research 10:45-54 Available from https://proceedings.mlr.press/v10/esseghir10a.html.

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