Abstract
Accurate classification of data sets is an important phenomenon for many applications. While multi-dimensionality to a certain point contributes to the classification performance, after a point, incorporating more attributes degrades the quality of the classification. In a pattern classification problem, by determining and excluding the least effective attribute(s) the performance of the classification is likely to improve. The task of the elimination of the least effective attributes in pattern classification is called ”data dimensionality reduction (DDR)”. DDR using Genetic Algorithms (DDR-GA) aims at discarding the less useful dimensions and re-organizing the data set by means of genetic operators. We show that a wise selection of the initial population improves the performance of the DDR-GA considerably and introduce a method to implement this approach. Our approach focuses on using information obtained a priori for the selection of initial chromosomes. Our work then compares the performance of the GA initiated by a randomly selected initial population to the performance of the ones initiated by a wisely selected one. Furthermore, the results indicate that our approach provides more accurate results compared to the purely random one in a reasonable amount of time.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
Bollacker, K.D., Ghosh, J.: Mutual information feature extractors for neural classifiers, IEEE Int. Conf. Neural Networks 3, 1528–1533 (1996)
Eads, D.R., Williams, S.J., Theiler, J., Porter, R., Harvey, N.R., Perkins, S.J., Brumby, S.P., David, N.A.: A multimodal approach to feature extraction for image and signal learning problems. In: Proceedings of SPIE (2004)
Fu, X., Wang, L.: Data Dimensionmality Reduction with Application to Simplify RBF Network Sutructure and Improving Classificaiton Performance. IEEE Transactions on Systems, Man and Cybernetics: Part B 33(3), 399–409 (2003)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Hallinan, J., Jackway, P.: Simultaneous evolution of feature subset and neural classifier on high-dimensional data. In: Conference on Digital Image Computing and Applications (1999)
Kambhatla, N., Leen, T.K.: Dimension reduction by local principal component analysis. Neural Computation 9, 1493–1516 (1997)
Kononenko, I.: Estimating Attributes: Analysis and Extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)
Lopez-Rubio, E., de Lazcano-Lobato, J.M.O., Munoz-Perez, J., Antonio Gomez-Ruiz, J.: Principal components analysis competitive learning. Neural Computation 16(11), 2459–2481 (2004)
Murphy, P.M., Aha, D.: UCI repository of machine learning databases (1994), http://www.ics.uci.edu/~mlearn/MLRepository.html
Ramos, V., Muge, F.: Less is more: Genetic optimisation of nearest neighbour classifiers. In: F. Muge, C. Pinto, and M. Piedade, editors, 10th Portuguese Conference on Pattern Recognition, pp. 293–301. Technical University of Lisbon (March 1988)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: an Introduction. MIT Press, Cambridge (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tümer, M.B., Demir, M.C. (2005). A Genetic Approach to Data Dimensionality Reduction Using a Special Initial Population. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_32
Download citation
DOI: https://doi.org/10.1007/11499305_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
Online ISBN: 978-3-540-31673-2
eBook Packages: Computer ScienceComputer Science (R0)