Abstract
This paper introduces the use of Harmony Search with novel fitness function in order to assign higher weights to informative features while noisy irrelevant features are given low weights. The fitness function is based on the Area Under the receiver operating characteristics Curve (AUC). The aim of this feature weighting is to improve the performance of the k-NN algorithm. Experimental results show that the proposed method can improve the classification performance of the k-NN algorithm in comparison with the other important method in realm of feature weighting such as Mutual Information, Genetic Algorithm, Tabu Search and chi-squared (χ 2). Furthermore, on synthetic data sets, this method is able to allocate very low weight to the noisy irrelevant features which may be considered as the eliminated features from the data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Blake, L., Merz, C.J.: UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html
Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., Panigrahi, B.K.: Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization. IEEE Transactions on Systems Man and Cybernetics Part B 41(1), 89–106 (2011)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis, vol. 7. Wiley (1973)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, vol. 12. Springer (2003), http://www.mitpressjournals.org/doi/abs/10.1162/evco.2004.12.2.269
Fawcett, T.: Roc graphs, Notes and practical considerations for data mining researchers ROC graphs. Intelligent Enterprise 31 (HPL-2003-4) 28 (2003)
Guvenir, H.A., Akkus, A.: Weighted k nearest neighbor classification feature projections. In: Proc. of the Twelfth International Symposium on Computer and Information Sciences, ISCIS XII, pp. 44–51 (1997)
Han, E.-H(S.), Karypis, G., Kumar, V.: Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 53–65. Springer, Heidelberg (2001), http://dl.acm.org/citation.cfm?id=646419.693652
Jankowski, N.: Discrete feature weighting selection algorithm. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 636–641 (2003)
Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation 188(2), 1567–1579 (2007)
Seeker, A., Freitas, A.: Wairs improving classification accuracy by weighting attributes in the airs classifier. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3759–3765 (2007)
Tang, P.H., Tseng, M.H.: Medical data mining using BGA and RGA for weighting of features in fuzzy k-NN classification. In: International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3070–3075 (2009)
Vivencio, D., Hruschka, E., Nicoletti, M., dos Santos, E., Galvao, S.: Feature-weighted k-nearest neighbor classifier. In: FOCI 2007, pp. 481–486 (2007)
Zomorodian, M.J., Adeli, A., Sinaee, M., Hashemi, S.: Improving Nearest Neighbor Classification by Elimination of Noisy Irrelevant Features. In: Horng, M.-F. (ed.) ACIIDS 2012, Part II. LNCS, vol. 7197, pp. 11–21. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Adeli, A., Sinaee, M., Zomorodian, M.J., Hamzeh, A. (2013). Harmony-Based Feature Weighting to Improve the Nearest Neighbor Classification. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-31552-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31551-0
Online ISBN: 978-3-642-31552-7
eBook Packages: EngineeringEngineering (R0)