-
1
-
-
0013326060
-
Feature selection for classification
-
M. Dash and H. Liu, Feature selection for classification, Intelligent Data Analysis 1(4) (1997) 131-156.
-
(1997)
Intelligent Data Analysis
, vol.1
, Issue.4
, pp. 131-156
-
-
Dash, M.1
Liu, H.2
-
3
-
-
77951139898
-
A discrete particle swarm optimization method for feature selection in binary classification problems
-
A. Unler and A. Murat, A discrete particle swarm optimization method for feature selection in binary classification problems, European Journal of Operational Research 206(3) (2010) 528-539.
-
(2010)
European Journal of Operational Research
, vol.206
, Issue.3
, pp. 528-539
-
-
Unler, A.1
Murat, A.2
-
4
-
-
0031381525
-
Wrappers for feature subset selection
-
R. Kohavi and G. H. John,Wrappers for feature subset selection, Artificial Intelligence 97 (1997) 273-324.
-
(1997)
Artificial Intelligence
, vol.97
, pp. 273-324
-
-
Kohavi, R.1
John, G.H.2
-
5
-
-
0015125457
-
A direct method of nonparametric measurement selection
-
A. Whitney, A direct method of nonparametric measurement selection, IEEE Trans- actions on Computers C20(9) (1971) 1100-1103.
-
(1971)
IEEE Trans- Actions on Computers C
, vol.20
, Issue.9
, pp. 1100-1103
-
-
Whitney, A.1
-
6
-
-
84914813506
-
On the effectiveness of receptors in recognition systems
-
T. Marill and D. Green, On the effectiveness of receptors in recognition systems, IEEE Transactions on Information Theory 9(1) 11-17 (1963).
-
(1963)
IEEE Transactions on Information Theory
, vol.9
, Issue.1
, pp. 11-17
-
-
Marill, T.1
Green, D.2
-
7
-
-
79958252965
-
An improved particle swarm optimization for feature selection
-
Y. Liu, G. Wang, H. Chen, and H. Dong, An improved particle swarm optimization for feature selection, Journal of Bionic Engineering 8(2) (2011) 191-200.
-
(2011)
Journal of Bionic Engineering
, vol.8
, Issue.2
, pp. 191-200
-
-
Liu, Y.1
Wang, G.2
Chen, H.3
Dong, H.4
-
8
-
-
8844244184
-
Genetic algorithm with fuzzy fitness function for feature selection in
-
B. Chakraborty, Genetic algorithm with fuzzy fitness function for feature selection, in IEEE Int. Symp. on Industrial Electronics (ISIE02), Vol. 1 (2002), pp. 315-319.
-
(2002)
IEEE Int Symp on Industrial Electronics ISIE02
, vol.1
, pp. 315-319
-
-
Chakraborty, B.1
-
9
-
-
67650675481
-
Genetic programming for feature subset ranking in binary classification problems
-
K. Neshatian andM. Zhang, Genetic programming for feature subset ranking in binary classification problems, in European Conference on Genetic Programming (2009), pp. 121-132.
-
(2009)
European Conference on Genetic Programming
, pp. 121-132
-
-
Neshatian, K.1
Zhang, M.2
-
10
-
-
0036530772
-
A fast and elitist multiobjective genetic algorithm: NSGA-II
-
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6(2) (2002) 182-197.
-
(2002)
IEEE Transactions on Evolutionary Computation
, vol.6
, Issue.2
, pp. 182-197
-
-
Deb, K.1
Pratap, A.2
Agarwal, S.3
Meyarivan, T.4
-
11
-
-
2942547409
-
SPEA2: Improving the strength pareto evolutionary algorithm
-
E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm, in Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (2002), pp. 95-100.
-
(2002)
Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems
, pp. 95-100
-
-
Zitzler, E.1
Laumanns, M.2
Thiele, L.3
-
16
-
-
84866876345
-
Binary particle swarm optimisation for feature selection: A filter based approach in
-
L. Cervante, B. Xue, M. Zhang, and L. Shang, Binary particle swarm optimisation for feature selection: A filter based approach, in IEEE Congress on Evolutionary Compu- tation (CEC2012) (2012), pp. 881-888.
-
(2012)
IEEE Congress on Evolutionary Compu- Tation (CEC2012
, pp. 881-888
-
-
Cervante, L.1
Xue, B.2
Zhang, M.3
Shang, L.4
-
19
-
-
0028496468
-
Learning boolean concepts in the presence of many irrelevant features
-
H. Almuallim and T. G. Dietterich, Learning boolean concepts in the presence of many irrelevant features, Artificial Intelligence 69 (1994) 279-305.
-
(1994)
Artificial Intelligence
, vol.69
, pp. 279-305
-
-
Almuallim, H.1
Dietterich, T.G.2
-
20
-
-
60249087028
-
Different metaheuristic strategies to solve the feature selection problem
-
S. C. Yusta, Different metaheuristic strategies to solve the feature selection problem, Pattern Recognition Letters 30 (2009) 525-534.
-
(2009)
Pattern Recognition Letters
, vol.30
, pp. 525-534
-
-
Yusta, S.C.1
-
22
-
-
0028547556
-
-
P. Pudil, J. Novovicova, and J. V. Kittler, Floating search methods in feature selection, Pattern Recognition Letters 15(11) (1994) 1119-1125.
-
(1994)
Floating Search Methods in Feature Selection, Pattern Recognition Letters
, vol.15
, Issue.11
, pp. 1119-1125
-
-
Pudil, P.1
Novovicova, J.2
Kittler, J.V.3
-
23
-
-
33748076461
-
A GA-based feature selection and parameters optimizationfor support vector machines
-
C.-L. Huang and C.-J. Wang, A GA-based feature selection and parameters optimizationfor support vector machines, Expert Systems with Applications 31(2) (2006) 231-240.
-
(2006)
Expert Systems with Applications
, vol.31
, Issue.2
, pp. 231-240
-
-
Huang, C.-L.1
Wang, C.-J.2
-
24
-
-
38049062940
-
Multi-objective feature selection with NSGA II
-
Springer Berlin Heidelberg
-
T. M. Hamdani, J.-M. Won, A. M. Alimi, and F. Karray, Multi-objective feature selection with NSGA II, in 8th Int. Conf. on Adaptive and Natural Computing Algorithms (ICANNGA07), Part I, Vol. 4431 (Springer Berlin Heidelberg, 2007), pp. 240-247.
-
(2007)
8th Int. Conf. on Adaptive and Natural Computing Algorithms (ICANNGA07), Part i
, vol.4431
, pp. 240-247
-
-
Hamdani, T.M.1
Won, J.-M.2
Alimi, A.M.3
Karray, F.4
-
25
-
-
76049126653
-
Multi-objective feature selection in QSAR using a machine learning approach
-
1509-1523
-
A. J. Soto, R. L. Cecchini, G. E. Vazquez, and I. Ponzoni, Multi-objective feature selection in QSAR using a machine learning approach, QSAR & Combinatorial Science 28(11-12) (2009) 1509-1523.
-
(2009)
QSAR & Combinatorial Science
, vol.28
, Issue.11-12
-
-
Soto, A.J.1
Cecchini, R.L.2
Vazquez, G.E.3
Ponzoni, I.4
-
26
-
-
69249205580
-
Parallel multiobjective memetic rbfnns design and feature selection for function approximation problems
-
A. Guillen, H. Pomares, J. Gonzalez, I. Rojas, O. Valenzuela, and B. Prieto, Parallel multiobjective memetic rbfnns design and feature selection for function approximation problems, Neurocomputing 72(16-18) (2009) 3541-3555.
-
(2009)
Neurocomputing
, vol.72
, Issue.16-18
, pp. 3541-3555
-
-
Guillen, A.1
Pomares, H.2
Gonzalez, J.3
Rojas, I.4
Valenzuela, O.5
Prieto, B.6
-
27
-
-
73549103101
-
Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications
-
B. Huang, B. Buckley, and T.-M. Kechadi, Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications, Expert Systems with Applications 37(5) (2010) 3638-3646.
-
Expert Systems with Applications 37(
, vol.5
, Issue.2010
, pp. 3638-3646
-
-
Huang, B.1
Buckley, B.2
Kechadi, T.-M.3
-
28
-
-
33847646332
-
Wrapper-filter feature selection algorithm using a memetic framework
-
Z. X. Zhu, Y. S. Ong, and M. Dash, Wrapper-filter feature selection algorithm using a memetic framework, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 37(1) (2007) 70-76.
-
(2007)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
, vol.37
, Issue.1
, pp. 70-76
-
-
Zhu, Z.X.1
Ong, Y.S.2
Dash, M.3
-
29
-
-
31744443319
-
Genetic programming for simultaneous feature selection and classifier design
-
D. Muni, N. Pal, and J. Das, Genetic programming for simultaneous feature selection and classifier design, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(1) (2006) 106-117.
-
(2006)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
, vol.36
, Issue.1
, pp. 106-117
-
-
Muni, D.1
Pal, N.2
Das, J.3
-
30
-
-
72749084593
-
Pareto front feature selection: Using genetic programming to explore feature space
-
New York, NY, USA
-
K. Neshatian and M. Zhang, Pareto front feature selection: Using genetic programming to explore feature space, in Proc. of the 11th Ann. Conf. on Genetic and Evolutionary Computation (GECCO09) (New York, NY, USA, 2009), pp. 1027-1034.
-
(2009)
Proc of the 11th Ann. Conf on Genetic and Evolutionary Computation (GECCO09)
, pp. 1027-1034
-
-
Neshatian, K.1
Zhang, M.2
-
31
-
-
33845523839
-
Feature selection based on rough sets and particle swarm optimization
-
X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen, Feature selection based on rough sets and particle swarm optimization, Pattern Recognition Letters, 28(4) (2007) 459-471.
-
(2007)
Pattern Recognition Letters
, vol.28
, Issue.4
, pp. 459-471
-
-
Wang, X.1
Yang, J.2
Teng, X.3
Xia, W.4
Jensen, R.5
-
33
-
-
78049357379
-
-
Springer Verlag Berlin, Heidelberg
-
M. A. Esseghir, G. Goncalves, and Y. Slimani, Adaptive particle swarm optimizer for feature selection, in Int. Conf. on Intelligent Data Engineering and Automated Learning (IDEAL10) (Springer Verlag, Berlin, Heidelberg, 2010), pp. 226-233.
-
(2010)
Adaptive Particle Swarm Optimizer for Feature Selection, in Int. Conf. on Intelligent Data Engineering and Automated Learning (IDEAL10)
, pp. 226-233
-
-
Esseghir, M.A.1
Goncalves, G.2
Slimani, Y.3
-
34
-
-
48749109333
-
Particle swarm optimization for parameter determination and feature selection of support vector machines
-
S. W. Lin, K. C. Ying, S. C. Chen, and Z. J. Lee, Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Systems with Applications 35(4) (2008) 1817-1824.
-
(2008)
Expert Systems with Applications
, vol.35
, Issue.4
, pp. 1817-1824
-
-
Lin, S.W.1
Ying, K.C.2
Chen, S.C.3
Lee, Z.J.4
-
35
-
-
62449087097
-
A rough set based hybrid method to feature selection, in Int
-
H. Ming, A rough set based hybrid method to feature selection, in Int. Symp. on Knowledge Acquisition and Modeling (KAM 08) (2008), pp. 585-588.
-
(2008)
Symp on Knowledge Acquisition and Modeling KAM 08
, pp. 585-588
-
-
Ming, H.1
-
36
-
-
33845621875
-
A hybrid approach for feature subset selection using neural networks and ant colony optimization
-
R. K. Sivagaminathan and S. Ramakrishnan, A hybrid approach for feature subset selection using neural networks and ant colony optimization, Expert Systems with Applications 33(1) (2007) 49-60.
-
(2007)
Expert Systems with Applications
, vol.33
, Issue.1
, pp. 49-60
-
-
Sivagaminathan, R.K.1
Ramakrishnan, S.2
-
37
-
-
28444441282
-
Ant colony optimization based network intrusion feature selection and detection, in Int
-
H. H. Gao, H. H. Yang, and X. Y. Wang, Ant colony optimization based network intrusion feature selection and detection, in Int. Conf. on Machine Learning and Cy- bernetics, Vol. 6 (2005), pp. 49-60.
-
(2005)
Conf on Machine Learning and Cy- Bernetics
, vol.6
, pp. 49-60
-
-
Gao, H.H.1
Yang, H.H.2
Wang, X.Y.3
-
39
-
-
33750714570
-
JavaEvA - A java framework for evolutionary algorithms
-
Centre for Bioinformatics, Tubingen, University of Tubingen
-
F. Streichert and H. Ulmer, JavaEvA - A java framework for evolutionary algorithms, Technical Report WSI-2005-06, Centre for Bioinformatics, Tubingen, University of Tubingen, (2005).
-
(2005)
Technical Report WSI-2005-06
-
-
Streichert, F.1
Ulmer, H.2
|