Abstract
Feature Ordering is a special training preprocessing for Incremental Attribute Learning (IAL), where features are trained one after another. Since most feature ordering calculation methods, compute feature ordering in one batch, no matter, this study presents a novel approach combining input feature ordered training and output partitioning for IAL to compute feature ordering with considering whether the output of the classification problem is univariate or multivariate. New metric called feature’s Single Sensibility (SS) is proposed to individually calculate features’ discrimination ability for each output. Finally, experimental benchmark results based on neural networks in IAL show that SS is applicable to calculates feature’s discrimination ability. Furthermore, combined output partitioning can also improve further the final classification performance effectively.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guan, S.U., Zhu, F.M.: An incremental approach to genetic-algorithms-based classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35, 227–239 (2005)
Zhu, F., Guan, S.U.: Ordered incremental training with genetic algorithms. Int. J. Intell. Syst. 19, 1239–1256 (2004)
Guan, S.U., Liu, J.: Incremental ordered neural network training. J. Intell. Syst. 12, 137–172 (2002)
Guan, S.U., Li, S.: Incremental learning with respect to new incoming input attributes. Neural Process. Lett. 14, 241–260 (2001)
Liu, X., Zhang, G., Zhan, Y., Zhu, E.: An incremental feature learning algorithm based on least square support vector machine. In: Preparata, F.P., Wu, X., Yin, J. (eds.) FAW 2008. LNCS, vol. 5059, pp. 330–338. Springer, Heidelberg (2008)
Bai, W., Cheng, S., Tadjouddine, E.M., Guan, S.-U.: Incremental attribute based particle swarm optimization. In: 2012 8th International Conference on Natural Computation, ICNC 2012, pp. 669–674. IEEE Computer Society, 29 May 2012–31 May 2012
Chao, S., Wong, F.: An incremental decision tree learning methodology regarding attributes in medical data mining. In: 2009 International Conference on Machine Learning and Cybernetics, pp. 1694–1699. IEEE Computer Society, 12 July 2009–15 July 2009
Ang, J.H., Guan, S.U., Tan, K.C., Al Mamun, A.: Interference-less neural network training. Neurocomputing 71, 3509–3524 (2008)
Guan, S.U., Liu, J.: Incremental neural network training with an increasing input dimension. J. Intell. Syst. 13, 45–69 (2004)
Wang, T., Guan, S.U.: Feature ordering for neural incremental attribute learning based on fisher’s linear discriminant. In: 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013, vol. 2, pp. 507–510. IEEE Computer Society, Hangzhou, China (2013)
Wang, T., Guan, S.-U., Liu, F.: Feature discriminability for pattern classification based on neural incremental attribute learning. In: Wang, Y., Li, T. (eds.) ISKE2011. AISC, vol. 122, pp. 275–280. Springer, Heidelberg (2011)
Wang, T., Guan, S.-U., Liu, F.: Entropic feature discrimination ability for pattern classification based on neural IAL. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part II. LNCS, vol. 7368, pp. 30–37. Springer, Heidelberg (2012)
Wang, T., Guan, S.U., Liu, F.: Correlation-based feature ordering for classification based on neural incremental attribute learning. Int. J. Mach. Learn. Comput. 2, 807–811 (2012)
Wang, T., Guan, S.U., Man, K.L., Ting, T.O., Lisitsa, A.: Optimized neural incremental attribute learning for pattern classification based on statistical discriminability. Int. J. Comput. Intell. Appl. 13, 1450019 (2014)
Wang, T., Guan, S.-U., Ting, T., Man, K.L., Liu, F.: Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning. In: Park, J.J., Zomaya, A., Yeo, S.-S., Sahni, S. (eds.) NPC 2012. LNCS, vol. 7513, pp. 482–491. Springer, Heidelberg (2012)
Wang, T., Wang, Y.: Pattern classification with ordered features using mRMR and neural networks. In: 2010 International Conference on Information, Networking and Automation, ICINA 2010, pp. V2128–V2131. IEEE Computer Society, 17 October 2010–19 October 2010
Guan, S.U., Li, P.: Incremental learning in terms of output attributes. J. Intell. Syst. 13, 95–122 (2004)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005)
Guan, S.U., Li, S.C.: Parallel growing and training of neural networks using output parallelism. IEEE Trans. Neural Netw. 13, 542–550 (2002)
Guan, S.U., Wang, K.: Hierarchical incremental class learning with output parallelism. J. Intell. Syst. 16, 167–193 (2007)
Acknowledgments
This research is supported by National Nature Science Foundation of China under Grant No. 61332007, China Jiangsu Provincial Science and Technology Foundation under Grant No. BK20131182, and China Postdoctoral Science Foundation under Grant No. 2015M571042.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, T., Guan, SU., Ma, J., Liu, F. (2015). Linear Feature Sensibility for Output Partitioning in Ordered Neural Incremental Attribute Learning. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-23862-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23861-6
Online ISBN: 978-3-319-23862-3
eBook Packages: Computer ScienceComputer Science (R0)