Abstract
The present article is devoted to experimental investigation of the performance of three machine learning algorithms for ITS dataset in their ability to achieve agreement with classes published in the biologi cal literature before. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel machine learning ap proaches to the analysis of datasets of this sort. This paper introduces a k-committees classifier and compares it with the discrete k-means and Nearest Neighbour classifiers. It turns out that all three machine learning algorithms are efficient and can be used to automate future biologically significant classifications for datasets of this kind. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented.
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
Bagirov, A.M., Rubinov, A.M., Yearwood, J.: A global optimization approach to classification. Optim. Eng. 3, 129–155 (2002)
Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (2001)
Huda, S., Ghosh, R., Yearwood, J.: A variable initialization approach to the EM algorithm for better estimation of the parameters of Hidden Markov Model based acoustic modeling of speech signals. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 416–430. Springer, Heidelberg (2006)
Huda, S., Yearwood, J., Ghosh, R.: A hybrid algorithm for estimation of the parameters of Hidden Markov Model based acoustic modeling of speech signals using constraint-based genetic algorithm and expectation maximization. In: Proceedings of ICIS 2007, the 6th Annual IEEE/ACIS International Conference on Computer and Information Science, Melbourne, Australia, July 11-13, pp. 438–443 (2007)
Huda, S., Yearwood, J., Togneri, R.: A constraint based evolutionary learning approach to the expectation maximization for optiomal estimation of the Hidden Markov Model for speech signal modeling. IEEE Transactions on Systems, Man, Cybernetics, Part B 39(1), 182–197 (2009)
Kang, B.H., Kelarev, A.V., Sale, A.H.J., Williams, R.N.: A new model for classifying DNA code inspired by neural networks and FSA. In: Hoffmann, A., Kang, B.-h., Richards, D., Tsumoto, S. (eds.) PKAW 2006. LNCS (LNAI), vol. 4303, pp. 187–198. Springer, Heidelberg (2006)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, New York (1990)
Kelarev, A.V., Kang, B.H., Sale, A.H.J., Williams, R.N.: Labeled directed graphs and FSA as classifiers of strings. In: 17th Australasian Workshop on Combinatorial Algorithms, AWOCA 2006, Uluru (Ayres Rock), Northern Territory, Australia, July 12–16, pp. 93–109 (2006)
Kelarev, A., Kang, B., Steane, D.: Clustering algorithms for ITS sequence data with alignment metrics. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1027–1031. Springer, Heidelberg (2006)
Lee, K., Kay, J., Kang, B.H.: KAN and RinSCut: lazy linear classifier and rank-in-score threshold in similarity-based text categorization. In: Proc. ICML 2002 Workshop on Text Learning, University of New South Wales, Sydney, Australia, pp. 36–43 (2002)
Park, G.S., Park, S., Kim, Y., Kang, B.H.: Intelligent web document classification using incrementally changing training data set. J. Security Engineering 2, 186–191 (2005)
Sattar, A., Kang, B.H.: Advances in Artificial Intelligence. In: Proceedings of AI 2006, Hobart, Tasmania (2006)
Steane, D.A., Nicolle, D., Mckinnon, G.E., Vaillancourt, R.E., Potts, B.M.: High-level relationships among the eucalypts are resolved by ITS-sequence data. Australian Systematic Botany 15, 49–62 (2002)
WEKA, Waikato Environment for Knowledge Analysis, http://www.cs.waikato.ac.nz/ml/weka
Washio, T., Motoda, H.: State of the art of graph-based data mining, SIGKDD Explorations. In: Dzeroski, S., De Raedt, L. (eds.) Editorial: Multi-Relational Data Mining: The Current Frontiers; SIGKDD Exploration 5(1), 59–68 (2003)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2005)
Yearwood, J.L., Mammadov, M.: Classification Technologies: Optimization Approaches to Short Text Categorization. Idea Group Inc., USA (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yearwood, J.L., Kang, B.H., Kelarev, A.V. (2009). Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset. In: Lee, Yh., Kim, Th., Fang, Wc., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2009. Lecture Notes in Computer Science, vol 5899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10509-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-10509-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10508-1
Online ISBN: 978-3-642-10509-8
eBook Packages: Computer ScienceComputer Science (R0)