The Experts below are selected from a list of 20934 Experts worldwide ranked by ideXlab platform
Mengjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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filter based Backward Elimination in wrapper based pso for feature selection in classification
Congress on Evolutionary Computation, 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
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IEEE Congress on Evolutionary Computation - Filter based Backward Elimination in wrapper based PSO for feature selection in classification
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
Hoai Bach Nguyen - One of the best experts on this subject based on the ideXlab platform.
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filter based Backward Elimination in wrapper based pso for feature selection in classification
Congress on Evolutionary Computation, 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
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IEEE Congress on Evolutionary Computation - Filter based Backward Elimination in wrapper based PSO for feature selection in classification
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
Licheng Jiao - One of the best experts on this subject based on the ideXlab platform.
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Sparse Gaussian Processes Using Backward Elimination
Lecture Notes in Computer Science, 2006Co-Authors: Ling Wang, Licheng JiaoAbstract:Gaussian Processes (GPs) have state of the art performance in regression. In GPs, all the basis functions are required for prediction; hence its test speed is slower than other learning algorithms such as support vector machines (SVMs), relevance vector machine (RVM), adaptive sparseness (AS), etc. To overcome this limitation, we present a Backward Elimination algorithm, called GPs-BE that recursively selects the basis functions for GPs until some stop criterion is satisfied. By integrating rank-1 update, GPs-BE can be implemented at a reasonable cost. Extensive empirical comparisons confirm the feasibility and validity of the proposed algorithm.
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ISNN (1) - Sparse gaussian processes using Backward Elimination
Advances in Neural Networks - ISNN 2006, 2006Co-Authors: Ling Wang, Licheng JiaoAbstract:Gaussian Processes (GPs) have state of the art performance in regression. In GPs, all the basis functions are required for prediction; hence its test speed is slower than other learning algorithms such as support vector machines (SVMs), relevance vector machine (RVM), adaptive sparseness (AS), etc. To overcome this limitation, we present a Backward Elimination algorithm, called GPs-BE that recursively selects the basis functions for GPs until some stop criterion is satisfied. By integrating rank-1 update, GPs-BE can be implemented at a reasonable cost. Extensive empirical comparisons confirm the feasibility and validity of the proposed algorithm.
Bing Xue - One of the best experts on this subject based on the ideXlab platform.
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filter based Backward Elimination in wrapper based pso for feature selection in classification
Congress on Evolutionary Computation, 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
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IEEE Congress on Evolutionary Computation - Filter based Backward Elimination in wrapper based PSO for feature selection in classification
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
Ivy Liu - One of the best experts on this subject based on the ideXlab platform.
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filter based Backward Elimination in wrapper based pso for feature selection in classification
Congress on Evolutionary Computation, 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
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IEEE Congress on Evolutionary Computation - Filter based Backward Elimination in wrapper based PSO for feature selection in classification
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hoai Bach Nguyen, Bing Xue, Ivy Liu, Mengjie ZhangAbstract:The advances in data collection increase the dimensionality of the data (i.e. the total number of features) in many fields, which arises a challenge to many existing feature selection approaches. This paper develops a new feature selection approach based on particle swarm optimisation (PSO) and a local search that mimics the typical Backward Elimination feature selection method. The proposed algorithm uses a wrapper based fitness function, i.e. the classification error rate. The local search is performed only on the global best and uses a filter based measure, which aims to take the advantages of both filter and wrapper approaches. The proposed approach is tested and compared with three recent PSO based feature selection algorithms and two typical traditional feature selection methods. Experiments on eight benchmark datasets show that the proposed algorithm can be successfully used to select a significantly smaller number of features and simultaneously improve the classification performance over using all features. The proposed approach outperforms the three PSO based algorithms and the two traditional methods.
