Graphics Reference
In-Depth Information
45. Derrac, J., Verbiest, N., García, S., Cornelis, C., Herrera, F.: On the use of evolutionary feature
selection for improving fuzzy rough set based prototype selection. Soft Comput. 17 (2), 223-
238 (2013)
46. Devi, V.S., Murty, M.N.: An incremental prototype set building technique. Pattern Recognit.
35 (2), 505-513 (2002)
47. Devijver, P.A., Kittler, J.: A Statistical Approach Pattern Recognition. Prentice Hall, New
Jersey (1982)
48. Devijver, P.A.: On the editing rate of the multiedit algorithm. Pattern Recogn. Lett. 4 , 9-12
(1986)
49. Domingo, C., Gavaldà, R., Watanabe, O.: Adaptive sampling methods for scaling up knowl-
edge discovery algorithms. Data Min. Knowl. Disc. 6 , 131-152 (2002)
50. Domingos, P.: Unifying instance-based and rule-based induction. Mach. Learn. 24 (2), 141-
168 (1996)
51. El-Hindi, K., Al-Akhras, M.: Smoothing decision boundaries to avoid overfitting in neural
network training. Neural Netw. World 21 (4), 311-325 (2011)
52. Fayed, H.A., Hashem, S.R., Atiya, A.F.: Self-generating prototypes for pattern classification.
Pattern Recognit. 40 (5), 1498-1509 (2007)
53. Fayed, H.A., Atiya, A.F.: A novel template reduction approach for the k-nearest neighbor
method. IEEE Trans. Neural Networks 20 (5), 890-896 (2009)
54. Fernández, F., Isasi, P.: Evolutionary design of nearest prototype classifiers. J. Heuristics
10 (4), 431-454 (2004)
55. Fernández, F., Isasi, P.: Local feature weighting in nearest prototype classification. IEEE
Trans. Neural Networks 19 (1), 40-53 (2008)
56. Ferrandiz, S., Boullé, M.: Bayesian instance selection for the nearest neighbor rule. Mach.
Learn. 81 (3), 229-256 (2010)
57. Franco, A., Maltoni, D., Nanni, L.: Data pre-processing through reward-punishment editing.
Pattern Anal. Appl. 13 (4), 367-381 (2010)
58. Fu, Z., Robles-Kelly, A., Zhou, J.: MILIS: multiple instance learning with instance selection.
IEEE Trans. Pattern Anal. Mach. Intell. 33 (5), 958-977 (2011)
59. Gagné, C., Parizeau, M.: Coevolution of nearest neighbor classifiers. IEEE Trans. Pattern
Anal. Mach. Intell. 21 (5), 921-946 (2007)
60. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles
for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE
Trans. Syst. Man Cybern. C 42 (4), 463-484 (2012)
61. Galar, M., Fernández, A., Barrenechea, E., Herrera, F.: Eusboost: enhancing ensembles for
highly imbalanced data-sets by evolutionary undersampling. Pattern Recognit. 46 (12), 3460-
3471 (2013)
62. García, S., Cano, J.R., Herrera, F.: A memetic algorithm for evolutionary prototype selection:
A scaling up approach. Pattern Recognit. 41 (8), 2693-2709 (2008)
63. García, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple
data sets" for all pairwise comparisons. J. Mach. Learn. Res. 9 , 2677-2694 (2008)
64. García, S., Cano, J.R., Bernadó-Mansilla, E., Herrera, F.: Diagnose of effective evolutionary
prototype selection using an overlapping measure. Int. J. Pattern Recognit. Artif. Intell. 23 (8),
1527-1548 (2009)
65. García, S., Fernández, A., Herrera, F.: Enhancing the effectiveness and interpretability of deci-
sion tree and rule induction classifiers with evolutionary training set selection over imbalanced
problems. Appl. Soft Comput. 9 (4), 1304-1314 (2009)
66. García, S., Herrera, F.: Evolutionary under-sampling for classification with imbalanced data
sets: Proposals and taxonomy. Evol. Comput. 17 (3), 275-306 (2009)
67. García, S., Derrac, J., Luengo, J., Carmona, C.J., Herrera, F.: Evolutionary selection of hyper-
rectangles in nested generalized exemplar learning. Appl. Soft Comput. 11 (3), 3032-3045
(2011)
68. García, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classifi-
cation: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34 (3), 417-435
(2012)
Search WWH ::




Custom Search