Abstract
Bio-inspired optimization algorithms have been successfully applied to solve many problems in engineering, science, and economics. In computer science bio-inspired optimization has different applications in different domains such as software engineering, networks, data mining, and many others. However, some applications may not be appropriate or even correct. In this paper we study this phenomenon through a particular method which applies the genetic algorithms on a time series classification task to set the weights of the similarity measures used in a combination that is used to classify the time series. The weights are supposed to be obtained by applying an optimization process that gives optimal classification accuracy. We show in this work, through examples, discussions, remarks, explanations, and experiments, that the aforementioned method of optimization is not correct and that completely randomly-chosen weights for the similarity measures can give the same classification accuracy.
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References
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming Modern Concepts and Practical Applications. Chapman and Hall/CRC (2009)
Bustos, B., Skopal, T.: Dynamic Similarity Search in Multi-metric Spaces. In: Proceedings of the ACM Multimedia, MIR Workshop, pp. 137–146. ACM Press, New York (2006)
Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vrani’c, D.: Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search. In: Proceedings of the IEEE International Workshop on Multimedia Content-based Analysis and Retrieval, pp. 514–521. IEEE Computer Society (2004)
Chen, L., Ng, R.: On the Marriage of Lp-Norm and Edit Distance. In: Proceedings of 30th International Conference on Very Large Data Base, Toronto, Canada (August 2004)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures. In: Proc of the 34th VLDB (2008)
Dohare, D., Devi, V.S.: Combination of Similarity Measures for Time Series Classification using Genetic Algorithms. Congress on Evolutionary Computation, CEC (2011)
Guo, A.Y., Siegelmann, H.: Time-warped Longest Common Subsequence Algorithm for Music Retrieval. In: Proc. ISMIR (2004)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms with CD-ROM. Wiley-Interscience (2004)
Keogh, E., Zhu, Q., Hu, B.: Hao. Y., Xi, X., Wei, L. and Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering, www.cs.ucr.edu/~eamonn/time_series_data/
Last, M., Kandel, A., Bunke, H. (eds.): Data Mining in Time Series Databases. World Scientific (2004)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Muhammad Fuad, M.M.: ABC-SG: A New Artificial Bee Colony Algorithm-Based Distance of Sequential Data Using Sigma Grams. In: The Tenth Australasian Data Mining Conference, AusDM 2012, Sydney, Australia, December 5-7 (2012)
Muhammad Fuad, M.M.: Genetic Algorithms-Based Symbolic Aggregate Approximation. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 105–116. Springer, Heidelberg (2012)
Muhammad Fuad, M.M.: Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS (LNAI), vol. 7713, pp. 443–455. Springer, Heidelberg (2012)
Muhammad Fuad, M.M.: Towards Normalizing the Edit Distance Using a Genetic Algorithms–Based Scheme. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS (LNAI), vol. 7713, pp. 477–487. Springer, Heidelberg (2012)
Muhammad Fuad, M.M.: Using Differential Evolution to Set Weights to Segments with Different Information Content in the Piecewise Aggregate Approximation. In: KES 2012. Frontiers of Artificial Intelligence and Applications (FAIA). IOS Press (2012)
Xiao, Y., Chen, D.Y., Ye, X.L.: Hu: Entropy-Based Symbolic Representation for Time Series Classification. Fuzzy Systems and Knowledge Discovery. In: Fourth International Conference on In Fuzzy Systems and Knowledge Discovery (2007)
Yamada, T., Yamashita, K., Ishii, N., Iwata, K.: Text Classification by Combining Different Distance Functions with Weights. In: International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, and International Workshop on Self-Assembling Wireless Networks (2006)
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Fuad, M.M.M. (2013). When Optimization Is Just an Illusion. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_11
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DOI: https://doi.org/10.1007/978-3-642-53914-5_11
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