The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Chid Apte - One of the best experts on this subject based on the ideXlab platform.
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Empirical evaluation of feature subset selection based on a real-world data set
Engineering Applications of Artificial Intelligence, 2004Co-Authors: Petra Perner, Chid ApteAbstract:Abstract Selecting the right set of features for classification is one of the most important problems in designing a Good Classifier. Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy, while some other statistical classification algorithm require the feature subset to be selected in a preprocessing phase. It is well known that correlated and irrelevant features may degrade the performance of the C4.5 algorithm. In our study, we evaluated the influence of feature pre-selection on the prediction accuracy of C4.5 using a real-world data set. We observed that accuracy of the C4.5 Classifier can be improved with an appropriate feature pre-selection phase for the learning algorithm.
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PKDD - Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set
Principles of Data Mining and Knowledge Discovery, 2000Co-Authors: Petra Perner, Chid ApteAbstract:Selecting the right set of features for classification is one of the most important problems in designing a Good Classifier. Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy while some other statistical classification algorithm require the feature subset to be selected in a preprocessing phase. It is well know that correlated and irrelevant features may degrade the performance of the C4.5 algorithm. In our study, we evaluated the influence of feature preselection on the prediction accuracy of C4.5 using a real-world data set. We observed that accuracy of the C4.5 Classifier can be improved with an appropriate feature preselection phase for the learning algorithm.
Petra Perner - One of the best experts on this subject based on the ideXlab platform.
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Empirical evaluation of feature subset selection based on a real-world data set
Engineering Applications of Artificial Intelligence, 2004Co-Authors: Petra Perner, Chid ApteAbstract:Abstract Selecting the right set of features for classification is one of the most important problems in designing a Good Classifier. Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy, while some other statistical classification algorithm require the feature subset to be selected in a preprocessing phase. It is well known that correlated and irrelevant features may degrade the performance of the C4.5 algorithm. In our study, we evaluated the influence of feature pre-selection on the prediction accuracy of C4.5 using a real-world data set. We observed that accuracy of the C4.5 Classifier can be improved with an appropriate feature pre-selection phase for the learning algorithm.
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PKDD - Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set
Principles of Data Mining and Knowledge Discovery, 2000Co-Authors: Petra Perner, Chid ApteAbstract:Selecting the right set of features for classification is one of the most important problems in designing a Good Classifier. Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy while some other statistical classification algorithm require the feature subset to be selected in a preprocessing phase. It is well know that correlated and irrelevant features may degrade the performance of the C4.5 algorithm. In our study, we evaluated the influence of feature preselection on the prediction accuracy of C4.5 using a real-world data set. We observed that accuracy of the C4.5 Classifier can be improved with an appropriate feature preselection phase for the learning algorithm.
Hamidah Ibrahim - One of the best experts on this subject based on the ideXlab platform.
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DEXA - Feature selection by ordered rough set based feature weighting
Lecture Notes in Computer Science, 2005Co-Authors: Qasem A. Al-radaideh, Nasir Sulaiman, Mohd Hasan Selamat, Hamidah IbrahimAbstract:The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a Good Classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough Classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.
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Feature selection by ordered rough set based feature weighting
Lecture Notes in Computer Science, 2005Co-Authors: Qasem A. Al-radaideh, Nasir Sulaiman, Mohd Hasan Selamat, Hamidah IbrahimAbstract:The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a Good Classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough Classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.
Qasem A. Al-radaideh - One of the best experts on this subject based on the ideXlab platform.
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DEXA - Feature selection by ordered rough set based feature weighting
Lecture Notes in Computer Science, 2005Co-Authors: Qasem A. Al-radaideh, Nasir Sulaiman, Mohd Hasan Selamat, Hamidah IbrahimAbstract:The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a Good Classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough Classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.
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Feature selection by ordered rough set based feature weighting
Lecture Notes in Computer Science, 2005Co-Authors: Qasem A. Al-radaideh, Nasir Sulaiman, Mohd Hasan Selamat, Hamidah IbrahimAbstract:The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a Good Classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough Classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.
Mohd Hasan Selamat - One of the best experts on this subject based on the ideXlab platform.
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DEXA - Feature selection by ordered rough set based feature weighting
Lecture Notes in Computer Science, 2005Co-Authors: Qasem A. Al-radaideh, Nasir Sulaiman, Mohd Hasan Selamat, Hamidah IbrahimAbstract:The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a Good Classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough Classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.
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Feature selection by ordered rough set based feature weighting
Lecture Notes in Computer Science, 2005Co-Authors: Qasem A. Al-radaideh, Nasir Sulaiman, Mohd Hasan Selamat, Hamidah IbrahimAbstract:The aim of feature subset selection is to reduce the complexity of an induction system by eliminating irrelevant and redundant features. Selecting the right set of features for classification task is one of the most important problems in designing a Good Classifier. In this paper we propose a feature selection approach based on rough set based feature weighting. In the approach the features are weighted and ranked in descending order. An incremental forward interleaved selection process is used to determine the best feature set with highest possible classification accuracy. The approach is experimented and tested using some standard datasets. The experiments carried out are to evaluate the influence of the feature pre-selection on the prediction accuracy of the rough Classifier. The results showed that the accuracy could be improved with an appropriate feature pre-selection phase.