Abstract
We deal with two important problems in pattern recognition that arise in the analysis of large datasets. While most feature subset selection methods use statistical techniques to preprocess the labeled datasets, these methods are generally not linked with the combinatorial properties of the final solutions. We prove that it is NP-hard to obtain an appropriate set of thresholds that will transform a given dataset into a binary instance of a robust feature subset selection problem. We address this problem using an evolutionary algorithm that learns the appropriate value of the thresholds. The empirical evaluation shows that robust subset of genes can be obtained. This evaluation is done using real data corresponding to the gene expression of lymphomas.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Davies, S., Russell, S.: NP-completeness of searches for smallest possible feature sets. In: Greiner, R., Subramanian, D. (eds.) AAAI Symposium on Intelligent Relevance, New Orleans, pp. 41–43. AAAI Press, Menlo Park (1994)
Downey, R., Fellows, M.: Parameterized Complexity. Springer, Heidelberg (1998)
Chen, J., Kanj, I., Jia, W.: Vertex cover: further observations and further improvements. In: Widmayer, P., Neyer, G., Eidenbenz, S. (eds.) WG 1999. LNCS, vol. 1665, pp. 313–324. Springer, Heidelberg (1999)
Downey, R., Fellows, M.: Fixed parameter tractability and completeness I: Basic theory. SIAM Journal of Computing 24, 873–921 (1995)
Cotta, C., Moscato, P.: The k-Feature Set problem is W[2]-complete. Journal of Computer and Systems Science 67, 686–690 (2003)
Harant, J., Pruchnewski, A., Voigt, M.: On dominating sets and independent sets of graphs. Combinatorics, Probability and Computing 8, 547–553 (1999)
Weihe, K.: Covering trains by stations or the power of data reduction. In: Battiti, R., Bertossi, A. (eds.) Proceedings of Algorithms and Experiments (Alex 98), Trento, Italy, pp. 1–8 (1998)
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)
Alizadeh, A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cotta, C., Sloper, C., Moscato, P. (2004). Evolutionary Search of Thresholds for Robust Feature Set Selection: Application to the Analysis of Microarray Data. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-24653-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21378-9
Online ISBN: 978-3-540-24653-4
eBook Packages: Springer Book Archive