Graphics Reference
In-Depth Information
144. Sebban, M., Nock, R., Brodley, E., Danyluk, A.: Stopping criterion for boosting-based data
reduction techniques: from binary to multiclass problems. J. Mach. Learn. Res.
3
, 863-885
(2002)
145. Segata, N., Blanzieri, E., Delany, S.J., Cunningham, P.: Noise reduction for instance-based
learning with a local maximal margin approach. J. Intell. Inf. Sys.
35
(2), 301-331 (2010)
146. Sierra, B., Lazkano, E., Inza, I., Merino, M., Larrañaga, P., Quiroga, J.: Prototype selection and
feature subset selection by estimation of distribution algorithms. a case study in the survival
of cirrhotic patients treated with TIPS. In: AIME '01: Proceedings of the 8th Conference on
AI in Medicine in Europe, Lecture Notes in Computer Science, vol. 2101, pp. 20-29 (2001)
147. Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing
algorithms. In: Proceedings of the Eleventh International Conference on Machine Learning,
pp. 293-301 (1994)
148. Steele, B.M.: Exact bootstrap k-nearest neighbor learners. Mach. Learn.
74
(3), 235-255
(2009)
149. Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man
Cybern.
6
(6), 448-452 (1976)
150. Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern.
6
(6), 769-772 (1976)
151. Triguero, I., García, S., Herrera, F.: IPADE: iterative prototype adjustment for nearest neighbor
classification. IEEE Trans. Neural Networks
21
(12), 1984-1990 (2010)
152. Triguero, I., García, S., Herrera, F.: Differential evolution for optimizing the positioning of
prototypes in nearest neighbor classification. Pattern Recognit.
44
(4), 901-916 (2011)
153. Triguero, I., Derrac, J., García, S., Herrera, F.: A taxonomy and experimental study on proto-
type generation for nearest neighbor classification. IEEE Trans. Syst. Man Cybern. C
42
(1),
86-100 (2012)
154. Tsai, C.F., Chang, C.W.: SVOIS: support vector oriented instance selection for text classifi-
cation. Inf. Syst.
38
(8), 1070-1083 (2013)
155. Tsai, C.F., Eberle, W., Chu, C.Y.: Genetic algorithms in feature and instance selection. Knowl.-
Based Syst.
39
, 240-247 (2013)
156. Ullmann, J.R.: Automatic selection of reference data for use in a nearest-neighbor method of
pattern classification. IEEE Trans. Inf. Theory
24
, 541-543 (1974)
157. Vascon, S., Cristani, M., Pelillo, M., Murino, V.: Using dominant sets for k-nn prototype
selection. In: International Conference on Image Analysis and Processing (ICIAP (2)), pp.
131-140 (2013)
158. Vázquez, F., Sánchez, J.S., Pla, F.: A stochastic approach to Wilson's editing algorithm. In:
2nd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Lecture Notes
in Computer Science, vol. 3523, pp. 35-42 (2005)
159. Verbiest, N., Cornelis, C., Herrera, F.: FRPS: a fuzzy rough prototype selection method.
Pattern Recognit.
46
(10), 2770-2782 (2013)
160. Wang, X., Miao, Q., Zhai, M.Y., Zhai, J.: Instance selection based on sample entropy for
efficient data classification with elm. In: International Conference on Systems, Man and
Cybernetics, pp. 970-974 (2012)
161. Wang, X.Z., Wu, B., He, Y.L., Pei, X.H.: NRMCS : Noise removing based on the MCS. In:
Proceedings of the Seventh International Conference on Machine Learning and Cybernetics,
pp. 89-93 (2008)
162. Wettschereck, D., Dietterich, T.G.: An experimental comparison of the nearest-neighbor and
nearest-hyperrectangle algorithms. Mach. Learn.
19
(1), 5-27 (1995)
163. Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weight-
ing methods for a class of lazy learning algorithms. Artif. Intell. Rev.
11
(1-5), 273-314
(1997)
164. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics
1
, 80-83 (1945)
165. Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans.
Syst. Man Cybern. B Cybern.
2
(3), 408-421 (1972)
166. Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Intell. Res.
6
, 1-34 (1997)
