SOFT COMPUTING FOR FEATURE SELECTION - Knowledge Mining Using Intelligent Agents - page 257

94. B. Metzger, N. Cho, S. Rston, and R. Radvany. Pregnancy weight and
antepartum insulin secretion predict glucose tolerance five years after
gestational diabetes mellitus, Diabetes Care . 16 , 1598-1605, (1993).
95. J. Handl and J. Knowles. Improvements to the scalability of multi-
objective clustering. In Proc. of the 2005 IEEE Congress on Evolutionary
Computation , pp. 2372-2379. IEEE Press, Anaheim, CA, (2005).
96. D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer. Co-clustering of biological
networks and gene expression data, Bioinformatics . 18 (90001), 145-154,
(2002).
97. J. Handl and J. Knowles. Feature subset selection in unsupervised learning
via multi-objective optimization, International Journal on Computational
Intelligence Research (to appear) . (2006).
98. J. Pena, J. Lozana, and P. Larranaga. An empirical comparison of four
initialization methods for the K -means algorithm, Pattern Recognition
Letters . 20 (10), 1027-1040, (1999).
99. J. Horn. The Nature of Niching: Genetic Algorithms and the Evolution
of Optimal, Cooperative Populations . PhD thesis, University of Illinois at
Urbana Champaign, Urbana, Illinois, (1997).
100. C. Blake, E. Keogh, and C. J. Merz. Uci repository of machine learning
databases, (1998).
101. M. Kirley and D. G. Green. An empirical investigation of optimization
in dynamic environments using the cellular genetic algorithm. In eds.
D. Whitley, D. Goldberg, E. Cant-Paz, L. Spector, I. Parmee, and H.-G.
Beyer, Proc. of the 2000 Genetic and Evolutionary Computation Conference
(GECCO-2000) , pp. 11-18, Las Vegas, Nevada, USA (July, 2000). Morgan
Kaufmann Publishers, San Francisco, California, USA.
102. M. Vidal-Naquet and S. Ullman. Object recognition with informative
features and linear classification, ICCV, . pp. 281-288, (2003).
103. C. Justin and R. Victor. Feature subset selection with a simulated annealing
data mining algorithm, J. Intelligent Information Systems . 9 , 57-81, (1997).
104. R. Caruana and D. Freitag. Greedy attribute selection. In Proc.ofthe
Eleventh International Conference on Machine Learning , pp. 28-36, (1994).
105. H. Vafaie and K. Jong. Genetic algorithms as a tool for restructuring feature
space representations. In Proc. of the Seventh International Conference on
Tools with Artificial Intelligence , Arlington, VA (November, 1995).
106. D. Goldberg. Genetic Algorithms in Search Optimization and Machine
Learning . (Addison-Wesley, 1989).
107. L. Prechelt. Proben1 — a set of neural network benchmark problems
and bench marking rules. Technical report 21/94, ftp://ftp.ira.uka.de/pub/
neuron/proben1.tar.gz, (1994).
108. Y. H. Chang, B. Zheng, X. H. Wang, and W. F. Good. Computer-aided
diagnosis of breast cancer using artificial neural networks: Comparison of
back propagation and genetic algorithms. In Proc. of International Joint
Conference on Neural Networks , Vol. 5, pp. 3674-3679, Washington, DC,
USA, (1999).