$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

On the Performance of Cuckoo Search and Bat Algorithms Based Instance Selection Techniques for SVM Speed Optimization with Application to e-Fraud Detection 원문보기

KSII Transactions on internet and information systems : TIIS, v.12 no.3, 2018년, pp.1348 - 1375  

AKINYELU, Andronicus Ayobami (School of Mathematics, Statistics & Computer Science University of KwaZulu-Natal) ,  ADEWUMI, Aderemi Oluyinka (School of Mathematics, Statistics & Computer Science University of KwaZulu-Natal)

Abstract AI-Helper 아이콘AI-Helper

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion ...

주제어

참고문헌 (63)

  1. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine learning, vol. 20, no. 3, pp. 273-297, September, 1995. 

  2. B. Yashvantrai Vyas, R. P. Maheshwari, and B. Das, "Pattern Recognition Application of Support Vector Machine for Fault Classification of Thyristor Controlled Series Compensated Transmission Lines," Journal of The Institution of Engineers (India): Series B, vol. 97, no. 2, pp. 175-183, June, 2016. 

  3. A. Bergholz, J. H. Chang, G. PaaB, F. Reichartz, and S. Strobel, "Improved Phishing Detection using Model-Based Features," in Proc. of the Conference on Email and Anti-Spam (CEAS), Mountain View, CA, pp. 1-27, August 21-22, 2008. 

  4. A. A. Akinyelu and A. O. Adewumi, "Classification of phishing email using random forest machine learning technique," Journal of Applied Mathematics, vol. 2014, Article ID 425731, 6 pages, April, 2014. 

  5. E. Kremic and A. Subasi, "Performance of random forest and SVM in face recognition," Int. Arab J. Inf. Technol., vol. 13, no. 2, pp. 287-293, March, 2016. 

  6. N. Panda, E. Y. Chang, and G. Wu, "Concept boundary detection for speeding up SVMs," in Proc. of the 23rd international conference on Machine learning, pp. 681-688, June 25 - 29, 2006. 

  7. J. A. Olvera-Lopez, J. A. Carrasco-Ochoa, J. F. Martinez-Trinidad, and J. Kittler, "A review of instance selection methods," Artificial Intelligence Review, vol. 34, no. 2, pp. 133-143, August, 2010. 

  8. S. Fine and K. Scheinberg, "Efficient SVM training using low-rank kernel representations," The Journal of Machine Learning Research, vol. 2, pp. 243-264, December, 2002. 

  9. B. L. Narayan, C. A. Murthy, and S. K. Pal, "Maxdiff kd-trees for data condensation," Pattern Recognition Letters, vol. 27, no. 3, pp. 187-200, February, 2006. 

  10. H. Liu and H. Motoda, "On Issues of Instance Selection," Data Mining and Knowledge Discovery, vol. 6, no. 2, pp. 115-130, April, 2002. 

  11. J. C. Bezdek and L. I. Kuncheva, "Nearest prototype classifier designs: An experimental study," International Journal of Intelligent Systems, vol. 16, no. 12, pp. 1445-1473, December, 2001. 

  12. V. Cerveron and F. J. Ferri, "Another move toward the minimum consistent subset: a tabu search approach to the condensed nearest neighbor rule," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 31, no. 3, pp. 408-413, June, 2001. 

  13. J. A. Olvera-Lopez, J. A. Carrasco-Ochoa, and J. F. Martinez-Trinidad, "Sequential search for decremental edition," in Proc. of International Conference on Intelligent Data Engineering and Automated Learning, pp. 280-285, July 6-8, 2005. 

  14. L. I. Kuncheva, "Fitness functions in editing k-NN reference set by genetic algorithms," Pattern Recognition, vol. 30, no. 6, pp. 1041-1049, June, 1997. 

  15. J. R. Cano, F. Herrera, and M. Lozano, "Stratification for scaling up evolutionary prototype selection," Pattern Recognition Letters, vol. 26, no. 7, pp. 953-963, May, 2005. 

  16. S. Garcia, J. R. Cano, and F. Herrera, "A memetic algorithm for evolutionary prototype selection: A scaling up approach," Pattern Recognition, vol. 41, no. 8, pp. 2693-2709, August, 2008. 

  17. I. M. Anwar, K. M. Salama, and A. M. Abdelbar, "Instance selection with ant colony optimization," Procedia Computer Science, vol. 53, pp. 248-256, January, 2015. 

  18. U. Garain, "Prototype reduction using an artificial immune model," Pattern Analysis and Applications, vol. 11, no. 3, pp. 353-363, September, 2008. 

  19. M. Behdad, L. Barone, M. Bennamoun, and T. French, "Nature-inspired techniques in the context of fraud detection," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 1273-1290, November, 2012. 

  20. KrebsOnSecurity. (2015), "FBI: $1.2B Lost to Business Email Scams". available at: http://krebsonsecurity.com/2015/08/fbi-1-2b-lost-to-business-email-scams/ (accessed 14-September - 2016). 

  21. T. N. Report. (2016, 01-August-2017). Card Fraud Worldwide. 12. Available: https://www.nilsonreport.com/upload/content_promo/The_Nilson_Report_10-17-2016.pdf 

  22. H. Brighton and C. Mellish, "Advances in instance selection for instance-based learning algorithms," Data mining and knowledge discovery, vol. 6, no. 2, pp. 153-172, April, 2002. 

  23. T. Reinartz, "A Unifying View on Instance Selection," Data Mining and Knowledge Discovery, vol. 6, no. 2, pp. 191-210, April, 2002. 

  24. J. Yang and S. Olafsson, "Optimization-based feature selection with adaptive instance sampling," Computers & Operations Research, vol. 33, no. 11, pp. 3088-3106, November, 2006. 

  25. C.-F. Tsai, W. Eberle, and C.-Y. Chu, "Genetic algorithms in feature and instance selection," Knowledge-Based Systems, vol. 39, pp. 240-247, February, 2013. 

  26. D. R. Wilson and T. R. Martinez, "Reduction Techniques for Instance-Based Learning Algorithms," Machine Learning, vol. 38, no. 3, pp. 257-286, March, 2000. 

  27. J. Chen, C. Zhang, X. Xue, and C.-L. Liu, "Fast instance selection for speeding up support vector machines," Knowledge-Based Systems, vol. 45, pp. 1-7, June, 2013. 

  28. H. Lei and V. Govindaraju, "Speeding up multi-class SVM evaluation by PCA and feature election," in Proc. of the Workshop on Feature Selection for Data Mining:Interfacing Machine Learning and Statistics Newport Beach, CA, April 22, 2005. 

  29. A. O. Adewumi and M. M. Ali, "A multi-level genetic algorithm for a multi-stage space allocation problem," Mathematical and Computer Modelling, vol. 51, no. 1, pp. 109-126, January, 2010. 

  30. T. R. Jensen and B. Toft, "Graph coloring problems," vol. 39, 2011. 

  31. S. Chetty and A. O. Adewumi, "Three new stochastic local search metaheuristics for the annual crop planning problem based on a new irrigation scheme," Journal of Applied Mathematics, vol. 2013, Article ID 158538, 14 pages, 2013., May, 2013. 

  32. O. A. Adewumi and A. A. Akinyelu, "A hybrid firefly and support vector machine classifier for phishing email detection," Kybernetes, vol. 45, no. 6, pp. 977-994, June, 2016. 

  33. X.-S. Yang and X. He, "Firefly algorithm: recent advances and applications," International Journal of Swarm Intelligence, vol. 1, no. 1, pp. 36-50, January, 2013. 

  34. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of IEEE international conference on neural networks, vol. 4, no. 2, pp. 1942-1948, November, 1995. 

  35. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," science, vol. 220, no. 4598, pp. 671-680, May, 1983. 

  36. X.-S. Yang and S. Deb, "Cuckoo search via Levy flights," in Proc. of World Congress on Nature & Biologically Inspired Computing, 2009. NaBIC 2009. , pp. 210-214, December 9-11, 2009. 

  37. X.-S. Yang, "A New Metaheuristic Bat-Inspired Algorithm," in Proc. of Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 65-74, 2010. 

  38. D. Rodrigues, L. A. M. Pereira, R. Y. M. Nakamura, K. A. P. Costa, X.-S. Yang, A. N. Souza, et al., "A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest," Expert Systems with Applications, vol. 41, no. 5, pp. 2250-2258, April, 2014. 

  39. S. A. Medjahed, T. A. Saadi, A. Benyettou, and M. Ouali, "Binary cuckoo search algorithm for band selection in hyperspectral image classification," IAENG International Journal of Computer Science, vol. 42, no. 3, pp. 183-191, July, 2015. 

  40. A. M. Taha, A. Mustapha, and S.-D. Chen, "Naive bayes-guided bat algorithm for feature selection," The Scientific World Journal, vol. 2013, Article ID 325973, 9 pages, 2013., December, 2013. 

  41. E. Emary, W. Yamany, and A. E. Hassanien, "New approach for feature selection based on rough set and bat algorithm," in Proc. of 9th International Conference on Computer Engineering & Systems (ICCES), pp. 346-353, December 22-23, 2014. 

  42. M. A. Laamari and N. Kamel, "A hybrid bat based feature selection approach for intrusion detection," in Proc. of Bio-Inspired Computing-Theories and Applications, ed: Springer, pp. 230-238, 2014. 

  43. R. R Rajalaxmi, "A Hybrid Binary Cuckoo Search and Genetic Algorithm for Feature Selection in Type-2 Diabetes," Current Bioinformatics, vol. 11, no. 4, pp. 490-499, September, 2016. 

  44. S. Mousavirad and H. Ebrahimpour-Komleh, "Wrapper feature selection using discrete cuckoo optimization algorithm," International Journal of Mechatronics Electrical, and Computer Engineering, vol. 4, no. 11, pp. 709-721, April, 2014. 

  45. K. Bache and M. Lichman. (2013), "UCI machine learning repository". available at: http://archive.ics.uci.edu/ml (accessed 12-May-2017). 

  46. C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, p. 27, April, 2011. 

  47. P. Graham., "A Plan for Spam," 2002. available at: http://www.paulgraham.com/spam.html (accessed 04-August-2016). 

  48. R. Shams and R. E. Mercer, "Classifying Spam Emails Using Text and Readability Features," in Proc. of IEEE 13th International Conference on Data Mining, pp. 657-666, December 7-10, 2013. 

  49. R. Duncan. "A Simple Guide to HTML," available at: http://www.simplehtmlguide.com/whatisht-ml.php (accessed 13-September-2016). 

  50. A. Almomani, T.-C. Wan, A. Altaher, A. Manasrah, E. ALmomani, M. Anbar, et al., "Evolving fuzzy neural network for phishing emails detection," Journal of Computer Science, vol. 8, no. 7, p. 1099, July, 2012. 

  51. I. Fette, N. Sadeh, and A. Tomasic, "Learning to detect phishing emails," in Proc. of the 16th international conference on World Wide Web, Banff, AB, Canada, pp. 649-656, May 8-12, 2007. 

  52. N. Zhang and Y. Yuan, "Phishing Detection Using Neural Network," CS229 lecture notes. 

  53. C. Group., "SpamAssassin Data," 2006. available at: http://www.csmining.org/index.php/spamassassin-datasets.html (accessed 05-August-2014). 

  54. J. Nazario., "Phishing Corpus," 2006. available at: http://monkey.org/jose/wiki/doku.php?idPhishingCorpus (accessed 27-April-2015). 

  55. A. Asuncion and D. Newman., "UCI Machine Learning Repository," 2007. available at: http://archive.ics.uci.edu/ml/datasets.html (accessed 15-August-2016). 

  56. Andrea., "Credit Card Fraud Detection," 2016. available at: https://www.kaggle.com/dalpozz/creditcardfraud (accessed 12-December-2016). 

  57. J. A. Olvera-Lopez, J. A. Carrasco-Ochoa, and J. F. Martinez-Trinidad, "A new fast prototype selection method based on clustering," Pattern Analysis and Applications, vol. 13, no. 2, pp. 131-141, May, 2010. 

  58. C. Chien-Hsing, K. Bo-Han, and C. Fu, "The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method," in Proc. of 18th International Conference on Pattern Recognition (ICPR'06), pp. 556-559, August 20-24, 2006. 

  59. T. Raicharoen and C. Lursinsap, "A divide-and-conquer approach to the pairwise opposite class-nearest neighbor (POC-NN) algorithm," Pattern Recognition Letters, vol. 26, no. 10, pp. 1554-1567, July, 2005. 

  60. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University.," no. 1-16, 2003. 

  61. X.-S. Yang. (2010), "Cuckoo Search (CS) Algorithm," available at: https://www.mathworks.com/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm/content/cuckoo_search.m (accessed 11-September-2016). 

  62. X.-S. Yang. (2015), "Bat Algorithm". available at: https://www.mathworks.com/matlabcentral/fileexchange/37582-bat-algorithm--demo-/content/bat_algorithm.m (accessed 11-September-2016). 

  63. M. Riyazuddin and V. V. S. S. S. Balaram, "Pattern Anonymization: Hybridizing Data Restructure with Feature Set Partitioning for Privacy Preserving in Supervised Learning," in Proc. of the First International Conference on Computational Intelligence and Informatics : ICCII 2016, S. C. Satapathy, V. K. Prasad, B. P. Rani, S. K. Udgata, and K. S. Raju, Eds., ed Singapore: Springer Singapore, pp. 603-614, 2017. 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로