Abstract
High accuracy and low overhead are two key features of a well-designed classifier for different classification scenarios. In this paper, we propose an improved classifier using a single-hidden layer feedforward neural network (SLFN) trained with extreme learning machine. The novel classifier first utilizes principal component analysis to reduce the feature dimension and then selects the optimal architecture of the SLFN based on a new localized generalization error model in the principal component space. Experimental and statistical results on the NSL-KDD data set demonstrate that the proposed classifier can achieve a significant performance improvement compared with previous classifiers.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abe S (2010) Support vector machines for pattern classification, 2nd edn. Springer, New York
Chaovalitwongse WA, Jeong YS, Jeong MK, Danish SF, Wong S (2011) Pattern recognition approaches for identifying subcortical targets during deep brain stimulation surgery. IEEE Intell Syst 26(5):54–63
Cheng C, Tay WP, Huang GB (2012) Extreme learning machines for intrusion detection. In: Proceedings of 2012 IJCNN, pp 1–8
Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892
Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
Lan Y, Hu Z, Soh YC, Huang GB (2013) An extreme learning machine approach for speaker recognition. Neural Comput Appl 22(3-4):417–425
Li K, Lu Z, Liu W, Yin J (2012) Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake. Pattern Recognit 45(4):1255–1264
Lin J, Yin J, Cai Z, Liu Q, Li K, Leung VCM (2013) A secure and practical mechanism for outsourcing elms in cloud computing. To be published in IEEE Intell Syst
Liu X, Wang L, Yin J, Zhu E, Zhang J (2013) An efficient approach to integrating radius information into multiple kernel learning. IEEE Trans Cybern 43(2):557–569
Moore DS, McCabe GP, Craig BA (2007) Introduction to the practice of statistics, 6th edn. W. H. Freeman and Company, New York
Sheikhan M, Jadidi Z, Farrokhi A (2012) Intrusion detection using reduced-size rnn based on feature grouping. Neural Comput Appl 21(6):1185–1190
Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: Proceedings of 2009 IEEE CISDA, pp 1–6
Wang XZ, Shao QY, Miao Q, Zhai JH (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9
Yeung DS, Ng WWY, Wang D, Tsang ECC, Wang XZ (2007) Localized generalization error model and its application to architecture selection for radial basis function neural network. IEEE Trans Neural Netw 18(5):1294–1305
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Natural Science Foundation of China (Grant No. 61170287, No. 60970034, No. 61070198, and No. 61379145).
Rights and permissions
About this article
Cite this article
Liu, Q., Yin, J., Leung, V.C.M. et al. Applying a new localized generalization error model to design neural networks trained with extreme learning machine. Neural Comput & Applic 27, 59–66 (2016). https://doi.org/10.1007/s00521-014-1549-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-014-1549-5