2010
DOI: 10.5120/169-295
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Feature Subset Selection Problem using Wrapper Approach in Supervised Learning

Abstract: Feature subset selection is of immense importance in the field of data mining. The increased dimensionality of data makes testing and training of general classification method difficult. Mining on the reduced set of attributes reduces computation time and also helps to make the patterns easier to understand. In this paper a wrapper approach for feature selection is proposed. As a part of feature selection step we used wrapper approach with Genetic algorithm as random search technique for subset generation ,wra… Show more

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Cited by 121 publications
(76 citation statements)
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“…We used the wrapper approach[40] where all subsets of features are evaluated using a given machine learning approach. The models resulting from the wrapper with each machine learning algorithm i were i = 1 , 2 ,…2 n , where n is the number of predictive factors.…”
Section: Methodsmentioning
confidence: 99%
“…We used the wrapper approach[40] where all subsets of features are evaluated using a given machine learning approach. The models resulting from the wrapper with each machine learning algorithm i were i = 1 , 2 ,…2 n , where n is the number of predictive factors.…”
Section: Methodsmentioning
confidence: 99%
“…The number of features (attributes) and number of instances in the raw dataset can be enormously large. Feature selection must be conducted to identify and remove irrelevant features [9]. Feature selection aims to maximize classification accuracy.…”
Section: Feature Selection and Ensemble Methodsmentioning
confidence: 99%
“…Wrapper method implants the model hypothesis search within the feature subset search [8]. Once a number of subsets of feature are obtained, each subset is to be evaluated with the classifier [33]. It has more risk of overfitting and is more computationally exhaustive.…”
Section: Feature Selection Methods For Gene Expression Datamentioning
confidence: 99%