Abstract
Feature selection is essentially important for high dimensional feature characterization problems. In this paper, we propose a weighted feature selection algorithm based on bacterial colony optimization (BCO) for dimensionality reduction. The weighted strategy is used for reducing the redundant features as well as increasing the classification performance, which considers the frequency of features being selected by bacterial colony optimization(BCO) as well as the repeated appearance in the same individual. The contributions of features in classification will be evaluated and kept in ‘Achieve’. The learning mechanism used in BCO considers the randomness which avoids the ignorance of unseen features as well as disengages from the local optimal error. Benchmark datasets with varying dimensionality are selected to test the effectiveness of the proposed feature selection method. The significance of the proposed weight feature selection algorithm is verified by comparing with three recently proposed population based feature selection algorithms.
This is a preview of subscription content, log in via an institution to check access.
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
Jin X., Gupta S., Mukherjee K., Ray A. :Wavelet-based feature extraction using probabilistic finite state automata for pattern classification. Pattern recognition, vol. 44, no. 7, pp. 1343-1356(2011).
Roislien, J.; Brita W.J.: Feature extraction across individual time series observations with spikes using wavelet principal component analysis. Statistics in medicine, vol.32, no.21 pp.3660-3669(2013)
Deng, H.; Runger, G.; Tuv, E.; Vladimir, M.: A time series forest for classification and feature extraction. Information Sciences, vol.239, pp. 142-153(2013)
Zhou, L., Lai, K.K., Yen, J.: Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation. Source of the document international journal of systems science, vol. 45, no.3, pp. 241-253(2014)
Khushaba, R.N., Al-Ani, A., Al-Jumaily, A.: Feature subset selection using differential evolution and a statistical repair mechanism. Expert systems with applications, vol. 38, no. 9 , pp. 11515 -11526(2011)
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Applied soft computing, Article in Press (2013)
Hu, Y., Ding, L., Xie, D., Wang, S.: The setting of parameters in an improved ant colony optimization algorithm for feature selection. Journal of computational information systems, vol.8, no.19, pp. 8231 -8238(2012)
Sivagaminathan, R.K., Ramakrishnan, S.: A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert systems with applications, vol. 33, no.1 pp. 49 -60(2007)
Sheikhan, M., Mohammadi, N.: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural computing and applications, vol. 23, no. 3-4, pp. 1185 -1194(2013)
Niu, B., Wang, H.: Bacterial Colony Optimization. Discrete dynamics in nature and society, vol.2012, no. 2012, pp.1-28(2013).
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, vol. 22, no. 3, pp. 52-67(2002)
Müller, S.D.,, Marchetto, J., Airaghi, S., and Koumoutsakos, P.: Optimization based on bacterial chemotaxis. IEEE transactions on evolutionary computation, vol. 6, no. 1, pp. 16-30(2002)
Peng, H., Long, F., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 8, pp.1226-1238(2005)
Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., and Jain, A.K.: Dimensionality reduction using genetic algorithm. IEEE Transaction on Evolutionary Computation, vol. 4, pp. 164-171(2000)
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE transactions on neural networks, vol.5, no.4, pp.537 -550(1994)
Zhang, Y., Zhang, Z.: Feature subset selection with cumulate conditional mutual information minimization. Expert systems with applications, vol. 39, no. 5, pp. 6078-6088(2012)
Schaffernicht, E., Gross, H.M.: Weighted mutual information for feature selection. In: Proceedings of artificial neural networks and machine learning, pp.181-188 (2011)
Dong, G., Guo, W.W., Tickle, K.: Solving the traveling salesman problem using cooperative genetic ant systems. Expert systems with applications, vol.39, no. 5, pp.5006 -5011
Zhang, C., Hu, H.: Ant colony optimization combining with mutual information for feature selection in support vector machines. Advances in artificial intelligence, lecture notes in computer science, pp. 918-921(2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, H., Jing, X., Niu, B. (2014). A Weighted Bacterial Colony Optimization for Feature Selection. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-09330-7_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
eBook Packages: Computer ScienceComputer Science (R0)