A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification

Cognitive Computation

Volumn 6, Issue 3, 2014, Pages 432-445

  Akusok, Anton ^a   Miche, Yoan ^a   Hegedus, Jozsef ^a   Nian, Rui ^d   Lendasse, Amaury ^a,b,c  

^a AALTO UNIVERSITY   (Finland)

^b BASQUE FOUNDATION FOR SCIENCE   (Spain)

^c ARCADA UNIVERSITY OF APPLIED SCIENCES   (Finland)

^d OCEAN UNIVERSITY OF CHINA   (China)

Author keywords

ELM; False positives; K NN; Malware detection

Indexed keywords

COGNITIVE SYSTEMS; PSYCHOLOGY COMPUTING;

CLASSIFICATION DECISION; DECISION MAKING MODELS; DECISION METHODOLOGY; ELM; EXTREME LEARNING MACHINE; FALSE POSITIVE; K-NN; MALWARE DETECTION;

DECISION MAKING;

EID: 84906946075     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-014-9253-4     Document Type: Article

References (35)

1. Lele S, Richtsmeier JT. Euclidean distance matrix analysis: a coordinate-free approach for comparing biological shapes using landmark data. Am J Phys Anthropol. 1991;86(3): 415-27.
2. Broder AZ, Glassman SC, Manasse MS, Zweig G. Syntactic clustering of the Web. Comput Netw ISDN Syst. 1997;29(8-13): 1157-66.
3. Broder AZ. On the resemblance and containment of documents. In: Compression and complexity of sequences (SEQUENCES'97). IEEE Computer Society; 1997. p. 21-29.
4. Robiah Y, Rahayu SS, Zaki MM, Shahrin S, Faizal MA, Marliza R. A new generic taxonomy on hybrid malware detection technique. arXiv. org cs. CR.
5. Srivastava A, Giffin J. Automatic discovery of parasitic malware. In: Jha S, Sommer R, Kreibich C, editors. Recent advances in intrusion detection (RAID'10). Berlin: Springer; 2010. p. 97-117.
6. Bailey M, Andersen J, Morleymao Z, Jahanian F. Automated classification and analysis of internet malware. In: Recent advances in intrusion detection (RAID'07); 2007.
7. F-Secure Corporation. F-Secure DeepGuard: a proactive response to the evolving threat scenario. 2006. http://www. f-secure. com/export/system/fsgalleries/white-papers/f-secure_deepguard_whitepaper-06-11-2006. pdf.
8. Willems C, Holz T, Freiling F. Toward automated dynamic malware analysis using CWSandbox. IEEE Secur Priv. 2007;5: 32-9.
9. Yoshioka K, Hosobuchi Y, Orii T, Matsumoto T. Vulnerability in public malware sandbox analysis systems. In: Proceedings of the 2010 10th IEEE/IPSJ international symposium on applications and the internet. Washington, DC: IEEE Computer Society; 2010. pp. 265-268.
10. Jaccard P. Étude comparative de la distribution florale dans une portion des alpes et du jura. Bulletin de la Société Vaudoise des Sciences Naturelles. 1901;37: 547-79.
11. Tan P-N, Steinbach M, Kumar V. Introduction to data mining. 1st ed. Boston: Addison Wesley; 2005.
12. Python. Python algorithms complexity. 2010. http://wiki. python. org/moin/TimeComplexity#sethttp://wiki. python. org/moin/TimeComplexity#set.
13. Carter JL, Wegman MN. Universal classes of hash functions. J Comput Syst Sci. 1979;18(2): 143-54.
14. Broder AZ, Charikar M, Frieze AM, Mitzenmacher M. Min-wise independent permutations. J Compu Syst Sci. 1998;60: 327-36.
15. Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1): 21-7.
16. Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing. 2006;70: 489-501.
17. Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern. 2012;42(2): 513-29.
18. Huang G-B, Zhu Q-Y, Mao KZ, Siew C-K, Saratchandran P, Sundararajan N. Can threshold networks be trained directly? IEEE Trans Circuits Syst II Express Briefs. 2006;53(3): 187-91.
19. Huang G-B, Chen L, Siew C-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw. 2006;17(4): 879-92.
20. Savitha R, Suresh S, Kim H. A meta-cognitive learning algorithm for an extreme learning machine classifier. Cogn Comput. (2013);1-11. doi: 10. 1007/s12559-013-9223-2.
21. Grassi M, Cambria E, Hussain A, Piazza F. Sentic web: a new paradigm for managing social media affective information. Cogn Comput. 2011;3(3): 480-9. doi: 10. 1007/s12559-011-9101-8.
22. Cambria E, Hussain A. Sentic album: content-, concept-, and context-based online personal photo management system. Cogn Comput. 2012;4(4): 477-96. doi: 10. 1007/s12559-012-9145-4.
23. Cambria E, Hussain A. Sentic computing: techniques, tools, and applications, springerBriefs in cognitive computation. Springer: Dordrecht; 2012. doi: 10. 1007/978-94-007-5070-8.
24. Wang Q-F, Cambria E, Liu C-L, Hussain A. Common sense knowledge for handwritten chinese text recognition. Cogn Comput. 2013;5(2): 234-42. doi: 10. 1007/s12559-012-9183-y.
25. Rao CR, Mitra SK. Generalized inverse of matrices and its applications. New York: Wiley; 1971.
26. Myers R. Classical and modern regression with applications. 2nd ed. Pacific Grove, CA: Duxbury Press; 1990.
27. Bontempi G, Birattari M, Bersini H, Recursive lazy learning for modeling and control. In: European conference on machine learning. 1998; pp. 292-303.
28. Miche Y, van Heeswijk M, Bas P, Simula O, Lendasse A. TROP-ELM: a double-regularized ELM using LARS and tikhonov regularization. Neurocomputing. 2011;74(16): 2413-21. doi: 10. 1016/j. neucom. 2010. 12. 042.
29. Group E. The op-elm toolbox. 2009. http://www. cis. hut. fi/projects/eiml/research/downloads/op-elm-toolbox.
30. Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A. OP-ELM: optimally-pruned extreme learning machine. IEEE Trans Neural Netw. 2010;21(1): 158-62. doi: 10. 1109/TNN. 2009. 2036259.
31. Miche Y, Bas P, Jutten C, Simula O, Lendasse A. A methodology for building regression models using extreme learning machine: OP-ELM. In: Verleysen M, editor. ESANN 2008, European Symposium on Artificial Neural Networks, Bruges, Belgium, d-side publ. Belgium: Evere; 2008. p. 247-52.
32. van Heeswijk M, Miche Y, Oja E, Lendasse A. GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing. 2011;74(16): 2430-7. doi: 10. 1016/j. neucom. 2010. 11. 034.
33. van Heeswijk M, Miche Y, Oja E, Lendasse A. Solving large regression problems using an ensemble of GPU-accelerated ELMs. In: Verleysen M, editor. ESANN2010: 18th European symposium on artificial neural networks, computational intelligence and machine learning, d-side publications. Belgium: Bruges; 2010. p. 309-14.
34. Lan Y, Soh YC, Huang G-B. Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing. 2010;73(16-18): 3191-9.
35. Frank A, Asuncion A. UCI machine learning repository. 2010. http://archive. ics. uci. edu/ml.