G. Ramesh, I. Krishnamurthi, K. Sampath Sree Kumar, An efficacious method for detecting phishing webpages through target domain identification, Decision Support Systems 61 (2014)12-22.
 R. Gowtham, I. Krishnamurthi, A comprehensive and efficacious architecture for detecting phishing webpages, computers & security 40 (2014 ) 23-3 7.
 E.-S. M. El-Alfy, A. A. AlHasan, Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm, Future Generation Computer Systems 64 (2016) 98-107.
 A. Abbasi, Z. Zhang, D. Zimbra, H. Chen, Detecting fake Websites: the contribution of statistical learning theory, MIS Q., 34 (3) (2010), 1-28.
 OpenDNS Phishing Quiz. https://www.opendns.com/phishing-quiz/, 2016 (accessed 19.10.16).
 APWG Phishing Attack Trends Reports, Retrieved April 21, 2015.
 R. M. Mohammad, F. Thabtah, L. McCluskey,Predicting phishing websites based on selfstructuring neural network, Neural Comput & Applic., 2013. DOI 10.1007/s00521-013-1490-z.
 N. Abdelhamid, A. Ayesh, F. Thabtah, Phishing detection based Associative Classification datamining, Expert Systems with Applications, 41(2014) 5948-5959.
 Y. Li, L. Yang, J. Ding, A minimum enclosing ball-based support vector machine approach for
detection of phishing websites, Optik, 127 (2016)345-351.
 X. Chen, I. Bose, A. Chung Man Leung, C. Guo,Assessing the severity of phishing attacks: A hybrid data mining approach, Decision Support Systems 50 (2011) 662-672.
 M. Aburrous, M.A. Hossain, K. Dahal, F. Thabtah, Intelligent phishing detection system for ebanking using fuzzy data mining, Expert Systems with Applications 37 (2010) 7913-7921.
 V. Ramanathan, H. Wechsler, Phishing detection and impersonated entity discovery using Conditional Random Field and Latent Dirichlet Allocation, computers & security 34 ( 2013 ) 123-139.
 N. Abdelhamid, Multi-label rules for phishing classification, Applied Computing and Informatics, Applied Computing and Informatics 11(2015)29-46.
 W. Hadi, F. Aburub, S. Alhawari, A new fast associative classification algorithm for detecting phishing websites, Applied Soft Computing 48(2016) 729-734.
 G. A. Montazer, S. ArabYarmohammadi, Detection of Phishing Attacks in Iranian E-banking Using a Fuzzy-Rough Hybrid System, Applied Soft Computing, 35 (2015) 482-492.
 P.A. Barraclough, M.A. Hossain, M.A. Tahir, G. Sexton, N. Aslam, Intelligent phishing detection and protection scheme for online transactions, Expert Systems with Applications 40(2013) 4697-4706.
 D. Zhu, G. Premkumar, X. Zhang, C.-.H. Chu, Data mining for network intrusion detection: a comparison of alternative methods, Decision Sciences 32 (4) (2001) 635-660.
 M. Imani, H. Ghassemian, Attribute Profile Based Feature Space Discriminant Analysis for Spectral-Spatial Classification of Hyperspectral Images, Computers and Electrical Engineering,2016, In Press.
 J. Lu, G. Wang, W. Deng and K. Jia, Reconstruction-Based Metric Learning for Unconstrained Face Verification, IEEE Transactions on Information Forensics and Security, 10(1) (2015) 79-89.
 N. Martinel, C. Micheloni and G. L. Foresti, Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning, IEEE Transactions on Image Processing, 24 (12) (2015) 5645-5658.
 H. Wang, L. Feng, J. Zhang and Y. Liu, Semantic Discriminative Metric Learning for Image Similarity Measurement, IEEE Transactions on Multimedia, 18 (8) (2016) 1579-1589.
 Q. Zhang, L. Zhang, Y. Yang, Y. Tian and L.Weng, Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction,IEEE Geoscience and Remote Sensing Letters,11 (3) (2014) 612-616.
 J. Lu, G. Wang and P. Moulin, Localized Multifeature Metric Learning for Image-Set-Based Face Recognition, IEEE Transactions on Circuits and Systems for Video Technology, 26 (3)(2016) 529-540.
 Y. Wang et al., Learning a Discriminative Distance Metric With Label Consistency for Scene Classification, IEEE Transactions on Geoscience and Remote Sensing, 55 (8) (2017) 4427-4440.
 K. Fukunaga, Introduction to Statistical Pattern Recognition, San Diego: Academic Press Inc, 1990.
 J.-G. Wang, E. Sung,W.-Y. Yau, Incremental two-dimensional linear discriminant analysis with applications to face recognition, Journal of Network and Computer Applications, 33 (2010)314-322.
 X. F. He, P. Niyogi, Locality preserving projections, in Proc. Adv. Neural Inf. Process. Syst. 16(2004) 153-160.
 Y.-L. Chang, J.-N. Liu, C.-C. Han, Y.-N. Chen,Hyperspectral Image Classification Using Nearest Feature Line Embedding Approach, IEEE Trans. Geoscience and remote sensing, 52 (1)(2014) 278-287.
 S. Z. Li, J. Lu, Face recognition using the nearest feature line method, IEEE Trans. Neural Netw.,10 (2) (1999) 439-433.
 Y. W. Pang, Y. Yuan, X. Li, Generalized nearest feature line for subspace learning, Electron. Lett.,43 (20) (2007) 1079-1080.
 J. Lu, Y. P. Tan, Uncorrelated discriminant nearest feature line analysis for face recognition, IEEE Signal Process. Lett., 17 (2) (2010) 185-188.
 W.-H. Yang, D.-Q. Dai, Two-Dimensional Maximum Margin Feature Extraction for Face Recognition, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 39 (4) (2009)1002-1012.
 Y.-L. Chang, A simulated annealing feature extraction approach for hyperspectral images, Future Generation Computer Systems 27 (2011)419-426.
 B. C. Kuo and D. A. Landgrebe, Nonparametric weighted feature extraction for classification, IEEE Trans. Geosci. Remote Sens, 42 (5) (2004)1096-1105.
 R. M. Mohammad, L. McCluskey, F. Thabtah,UCI Machine Learning Repository: Phishing Websites Data Set. http://archive.ics.uci.edu/ml/datasets/Phishing+Websites#, 2015(accessed 2.10.16).