The emerging ?Big dimensionality?

IEEE Computational Intelligence Magazine

Volumn 9, Issue 3, 2014, Pages 14-26

  Zhai, Yiteng ^a   Ong, Yew Soon ^a   Tsang, Ivor W ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

DATA ANALYTICS; DATA REPOSITORIES; DATA VOLUME; GRAND CHALLENGE; RESEARCH COMMUNITIES; SCALABILITY ISSUE;

BIG DATA;

EID: 84904651375     PISSN: 1556603X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCI.2014.2326099     Document Type: Article

References (123)

1. IBM big data success stories, IBM, Armonk, NY, Tech. Rep., Oct. 2011.
2. IBM big data platform-Bringing big data to the enterprise. [Online]. Available: http://www.ibm.com/software/data/bigdata/
3. P. C. Zikopoulos, D. deRoos, K. Parasuraman, T. Deutsch, D. Corrigan, and J. Giles, Harness the Power of Big Data the IBM Big Data Platform, R. B. Melnyk, Ed. New York: McGraw-Hill, 2012.
4. D. E. O'Leary, "Artificial intelligence and big data," IEEE Intell. Syst., vol. 28, no. 2, pp. 96-99, 2013.
5. M. Ferguson, "Architecting a big data platform for analytics," A whitepaper prepared for IBM, Armonk, New York, 2012.
6. J. Gantz and D. Reinsel. (2011, June). Extracting value from chaos, std. [Online]. Available: http://www.emc.com/collateral/analyst-reports/idc- extracting-value-from-chaos-ar.pdf
7. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers, "Big data: The next frontier for innovation, competition and productivity," McKinsey Global Inst., Tech. Rep., May 2011.
8. P. Kröger, "Going big in data dimensionality-Challenges and solutions in mining high dimensional data," in Proc. Symp. Scalable Analytics, Garching bei München, Germany, Nov. 2012.
9. A. Garg, S. Har-Peled, and D. Roth, "On generalization bounds, projection profile, and margin distribution," in Proc. Int. Conf. Machine Learning, Sydney, Australia, 2002, pp. 171-178.
10. R. J. Durrant and A. Kabán, "Sharp generalization error bounds for randomly-projected classifiers," in Proc. 30th Int. Conf. Machine Learning, Atlanta, GA, 2013, pp. 693-701.
11. P. J. Bickel and E. Levina, "Some theory for Fishers linear discriminant function, Naive Bayes, and some alternatives when there are many more variables than observations," Bernoulli, vol. 10, no. 6, pp. 989-1010, Dec. 2004. (Pubitemid 44242745)
12. E. Candes and T. Tao, "The Dantzig selector: Statistical estimation when p is much larger than n," Ann. Statist., vol. 35, no. 6, pp. 2313-2351, Dec. 2007.
13. Q. Mao and I. W. Tsang, "Efficient multi-template learning for structured prediction," IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 2, pp. 248-261, 2013.
14. P. Gehler and S. Nowozin, "On feature combination for multiclass object classification," in Proc. IEEE 12th Int. Conf. Computer Vision, Kyoto, Japan, 2009, pp. 221-228.
15. (2013, Dec.). The 1st International Workshop High Dimensional Data Mining. IEEE Int. Conf. Data Mining. Dallas, Texas. [Online]. Available: http://www.cs.bham.ac.uk/~axk/HDM.htm
16. (2014). EC generalisation in high-dimensional input spaces. Special Session for WCCI 2014. [Online]. Available: http://ieee-cis.blogspot.sg/2013/11/ call-for-papers-special-session-for.html
17. J. Huang and C. X. Ling, "Constructing new and better evaluation measures for machine learning," in Proc. Int. Joint Conf. Artificial Intelligence, Hyderabad, India, 2007.
18. Q. Mao and I. W. Tsang, "A feature selection method for multivariate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, 2013.
19. H. Leather, E. Bonilla, and M. O'Boyle, "Automatic feature generation for machine learning based optimizing compilation," in Proc. 7th Annu. IEEE/ACM Int. Symp. Code Generation Optimization, Seattle, WA, Mar. 2009, pp. 81-91.
20. Y. Sun and B. Bhanu, "Image retrieval with feature selection and relevance feedback," in Proc. 2010 17th IEEE Int. Conf. Image Processing, Hong Kong, China, 2010, pp. 3209-3212.
21. M. Duggan and L. Rainie, "Cell phone activities 2012," Washington, D.C., Tech. Rep., Nov. 2012.
22. W. Roush. (2007, Mar. 12). TR10: Peering into video's future. MIT Technology Review. Cambridge, MA. [Online]. Available: http://www. technologyreview.com/Infotech/18284/
23. (2013). YouTube Statistic. [Online]. Available: http://www.youtube.com/ yt/press/statistics.html
24. A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001. (Pubitemid 32680801)
25. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Computer Society Conf. Computer Vision Pattern Recognition, San Diego, CA, 2005, pp. 886-893. (Pubitemid 43897286)
26. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Nov. 2004.
27. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "Speeded-up robust features (SURF)," J. Comput. Vis. Image Underst., vol. 110, no. 3, pp. 346-359, 2008.
28. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615-1630, 2005. (Pubitemid 41555406)
29. A. Vedaldi and S. Soatto, "Quick shift and kernel methods for mode seeking," in Proc. European Conf. Computer Vision, Marseille, France, 2008, pp. 705-718.
30. J. L. Crowley and A. C. Parker, "A representation for shape based on peaks and ridges in the difference of low pass transform," IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 2, pp. 156-170, Mar. 1984.
31. I. Laptev, M. Marsza?ek, C. Schmid, and B. Rozenfeld, "Learning realistic human actions from movies," in Proc. IEEE Conf. Computer Vision Pattern Recognition, Anchorage, AK, 2008, pp. 1-8.
32. G. Toderici, H. Aradhye, M. Pasca, L. Sbaiz, and J. Yagnik, "Finding meaning on You-Tube: Tag recommendation and category discovery," in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2010, pp. 3447-3454.
33. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu, "Action recognition by dense trajectories," in Proc. IEEE Conf. Computer Vision Pattern Recognition, Colorado Springs, CO, June 2011, pp. 3169-3176.
34. Z. Xu, Y. Yang, I. W. Tsang, N. Sebe, and A. Hauptmann, "Feature weighting via optimal thresholding for video analysis," in Proc. IEEE Int. Conf. Computer Vision, Sydney, Australia, Dec. 2013, pp. 3440-3447.
35. R. Díaz-Uriarte and S. A. de Andrés, "Gene selection and classification of microarray data using random forest," BMC Bioinformatics, vol. 7, no. 3, 2006.
36. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine Learn., vol. 46, nos. 1-3, pp. 389-422, 2002. (Pubitemid 34129977)
37. K. Bache and M. Lichman. (2013). UCI machine learning repository. Dept. Information Comput. Sci., Univ. California. Irvine, CA. [Online]. Available: http://archive.ics.uci.edu/ml
38. S. Hettich and S. D. Bay. (1999). The UCI KDD archive. Dept. Inform. Comput. Sci., Univ. California. Irvine, CA. [Online]. Available: http://kdd.ics.uci.edu
39. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, 2011.
40. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 1993.
41. H. Liu and R. Setiono, "Chi2: Feature selection and discretization of numeric attributes," in Proc. Int. Conf. Tools with Artificial Intelligence, Herndon, VA, 1995, pp. 388-391.
42. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander, "Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
43. Z. Zhao, F. Morstatter, S. Sharma, S. Alelyani, A. Anand, and H. Liu, "Advancing feature selection research," Arizona State Univ., Phoenix, AZ, Tech. Rep., 2011.
44. F. Nie, S. Xiang, Y. Jia, C. Zhang, and S. Yan, "Trace ratio criterion for feature selection," in Proc. AAAI 23rd Nat. Conf. Artificial Intelligence, Chicago, IL, 2008, vol. 2, pp. 671-676.
45. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, 2005. (Pubitemid 41245053)
46. V. N. Vapnik, Statistical Learning Theory. S. Haykin, Ed. New York: Wiley, Sept. 1998.
47. A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artif. Intell., vol. 97, nos. 1-2, pp. 245-271, 1997. (Pubitemid 127401106)
48. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, Mar. 2003.
49. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, "1-norm support vector machines," in Neural Information Processing Systems. Vancouver, BC: MIT Press, 2003.
50. P. Bradley and O. L. Mangasarian, "Feature selection via concave minimization and support vector machines," in Proc. 15th Int. Conf. Machine Learning, Madison, WI: Morgan Kaufmann, 1998, pp. 82-90.
51. G. M. Fung and O. L. Mangasarian, "A feature selection Newton method for support vector machine classification," Comput. Optim. Applicat., vol. 28, no. 2, pp. 185-202, July 2004.
52. M. Tan, L. Wang, and I. W. Tsang, "Learning sparse SVM for feature selection on very high dimensional datasets," in Proc. 27th Int. Conf. Machine Learning, Haifa, Israel, 2010, pp. 1047-1054.
53. Y. Zhai, M. Tan, I. W. Tsang, and Y.-S. Ong, "Discovering support and affiliated features from very high dimensions," in Proc. 29th Int. Conf. Machine Learning, J. Langford and J. Pineau, Eds. Edinburgh, Scotland, U.K.: Omnipress, July 2012, pp. 1455-1462.
54. J. T. Dudley and A. J. Butte, "Identification of discriminating biomarkers for human disease using integrative network biology," in Proc. Pacific Symp. Biocomputing, 2009, pp. 27-38.
55. S. Kim and E. P. Xing, "Statistical estimation of correlated genome associations to a quantitative trait network," PLoS Genet., vol. 5, no. 8, p. e1000587, 2009.
56. H. D. Bondell and B. J. Reich, "Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR," Biometrics, vol. 64, pp. 115-123, Mar. 2008. (Pubitemid 351316873)
57. L. W. Zhong and J. T. Kwok, "Efficient sparse modeling with automatic feature grouping," in Proc. Int. Conf. Machine Learning, Bellevue, WA, 2011, pp. 9-16.
58. S. Yang, L. Yuan, Y.-C. Lai, X. Shen, P. Wonka, and J. Ye, "Feature grouping and selection over an undirected graph," in Proc. 18th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, Beijing, China, Aug. 2012, pp. 922-930.
59. S. Orrú, E. Giuressi, M. Casula, A. Loizedda, R. Murru, M. Mulargia, M. Masala, D. Cerimele, M. Zucca, N. Aste, P. Biggio, C. Carcassi, and L. Contu, "Psoriasis is associated with a SNP haplotype of the corneodesmosin gene (CDSN)," Tissue Antigens, vol. 60, no. 4, pp. 292-298, 2002. (Pubitemid 36020801)
60. A. J. Brookes, "The essence of SNPs," Gene, vol. 234, no. 2, pp. 177-186, July 1999. (Pubitemid 29298180)
61. B.-Y. Sun, X. Zhang, J. Li, and X.-M. Mao, "Feature fusion using locally linear embedding for classification," IEEE Trans. Neural Networks, vol. 21, no. 1, pp. 163-168, 2010.
62. J. Gao, J. Zhang, and D. Tien, "Relevance units latent variable model and nonlinear dimensionality reduction," IEEE Trans. Neural Networks, vol. 21, no. 1, pp. 123-135, 2010.
63. S. W. Lee and Z. Bien, "Representation of a Fisher criterion function in a kernel feature space," IEEE Trans. Neural Networks, vol. 21, no. 2, pp. 333-339, 2010.
64. P. Pulkkinen and H. Koivisto, "A dynamically constrained multiobjective genetic fuzzy system for regression problems," IEEE Trans. Fuzzy Syst., vol. 18, no. 1, pp. 161-177, 2010.
65. Y. Aksu, D. J. Miller, G. Kesidis, and Q. X. Yang, "Margin- maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions," IEEE Trans. Neural Networks, vol. 5, no. 21, pp. 710-717, May 2010.
66. Z. Zhu, S. Jia, and Z. Ji, "Towards a memetic feature selection paradigm," IEEE Comput. Intell. Mag., vol. 5, no. 2, pp. 41-53, May 2010.
67. L. Zhou, L. Wang, and C. Shen, "Feature selection with redundancy-constrained class separability," IEEE Trans. Neural Networks, vol. 21, no. 5, pp. 853-858, 2010.
68. D. Lim, Y. Jin, Y.-S. Ong, and B. Sendhoff, "Generalizing surrogate-assisted evolutionary computation," IEEE Trans. Evol. Comput., vol. 14, no. 3, pp. 329-355, May 2010.
69. M. J. Gacto, R. Alcalá, and F. Herrera, "Integration of an index to preserve the semantic interpretability in the multiobjective evolutionary rule selection and tuning of linguistic fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 18, no. 3, pp. 515-531, 2010.
70. K. Zhang and J. T. Kwok, "Clustered Nyström method for large scale manifold learning and dimension reduction," IEEE Trans. Neural Networks, vol. 21, no. 10, pp. 1576-1587, 2010.
71. H. Huang and H. He, "Super-resolution method for face recognition using nonlinear mappings on coherent features," IEEE Trans. Neural Networks, vol. 22, no. 1, pp. 121-130, 2011.
72. M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, "Enhancing differential evolution utilizing proximity-based mutation operators," IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 99-119, 2011.
73. X. Yang, G. Zhang, J. Lu, and J. Ma, "A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises," IEEE Trans. Fuzzy Syst., vol. 19, no. 1, pp. 105-115, 2011.
74. C.-H. Li, B.-C. Kuo, and C.-T. Lin, "LDA-based clustering algorithm and its application to an unsupervised feature extraction," IEEE Trans. Fuzzy Syst., vol. 19, no. 1, pp. 152-163, 2011.
75. Z. Deng, K.-S. Choi, F.-L. Chung, and S. Wang, "Scalable TSK fuzzy modeling for very large datasets using minimal-enclosing-ball approximation," IEEE Trans. Fuzzy Syst., vol. 19, no. 2, pp. 210-226, 2011.
76. R. N. Mahdi and E. C. Rouchka, "Reduced HyperBF networks: Regularization by explicit complexity reduction and scaled Rprop-based training," IEEE Trans. Neural Networks, vol. 22, no. 5, pp. 673-686, 2011.
77. J.-B. Yang and C. J. Ong, "Feature selection using probabilistic prediction of support vector regression," IEEE Trans. Neural Networks, vol. 22, no. 6, pp. 954-962, 2011.
78. H. K. Singh, A. Isaacs, and T. Ray, "A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems," IEEE Trans. Evol. Comput., vol. 15, no. 4, pp. 539-556, 2011.
79. R. Alcalá, M. J. Gacto, and F. Herrera, "A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems," IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 666-681, 2011.
80. B. Wang, S. Wang, and J. Watada, "Fuzzy-portfolio-selection models with value-at-risk," IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 758-769, 2011.
81. J. Alcalá-Fdez, R. Alcalá, and F. Herrera, "A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning," IEEE Trans. Fuzzy Syst., vol. 19, no. 5, pp. 857-872, 2011.
82. F. Oveisi, S. Oveisi, A. Erfanian, and I. Patras, "Tree-structured feature extraction using mutual information," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 1, pp. 127-137, 2012.
83. Q. Hu, W. Pan, L. Zhang, D. Zhang, Y. Song, M. Guo, and D. Yu, "Feature selection for monotonic classification," IEEE Trans. Fuzzy Syst., vol. 20, no. 1, pp. 69-81, 2012.
84. Y. Huang, D. Xu, and F. Nie, "Semi-supervised dimension reduction using trace ratio criterion," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 3, pp. 519-526, 2012.
85. A. Stuhlsatz, J. Lippel, and T. Zielke, "Feature extraction with deep neural networks by a generalized discriminant analysis," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 4, pp. 596-608, 2012.
86. X. Li and X. Yao, "Cooperatively coevolving particle swarms for large scale optimization," IEEE Trans. Evol. Comput., vol. 16, no. 2, pp. 210-224, 2012.
87. M. Antonelli, P. Ducange, and F. Marcelloni, "Genetic training instance selection in multiobjective evolutionary fuzzy systems: A coevolutionary approach," IEEE Trans. Fuzzy Syst., vol. 20, no. 2, pp. 276-290, 2012.
88. Y. Hou, I. Song, H.-K. Min, and C. H. Park, "Complexity-reduced scheme for feature extraction with linear discriminant analysis," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 6, pp. 1003-1009, 2012.
89. E. K. Burke, M. R. Hyde, and G. Kendall, "Grammatical evolution of local search heuristics," IEEE Trans. Evol. Comput., vol. 16, no. 3, pp. 406-417, 2012.
90. T. Blackwell, "A study of collapse in bare bones particle swarm optimization," IEEE Trans. Evol. Comput., vol. 16, no. 3, pp. 354-372, 2012.
91. C.-W. Seah, I. W. Tsang, and Y.-S. Ong, "Transductive ordinal regression," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 7, pp. 1074-1086, 2012.
92. Y.-C. Chen, N. R. Pal, and I.-F. Chung, "An integrated mechanism for feature selection and fuzzy rule extraction for classification," IEEE Trans. Fuzzy Syst., vol. 20, no. 4, pp. 683-698, 2012.
93. F. Hassanzadeh, M. Collan, and M. Modarres, "A practical approach to R&D portfolio selection using the fuzzy pay-off method," IEEE Trans. Fuzzy Syst., vol. 20, no. 4, pp. 615-622, 2012.
94. L. W. Zhong and J. T. Kwok, "Efficient sparse modeling with automatic feature grouping," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 9, pp. 1436-1447, 2012.
95. P. Padungweang, C. Lursinsap, and K. Sunat, "A discrimination analysis for unsupervised feature selection via optic diffraction principle," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 10, pp. 1587-1600, 2012.
96. M. Ramona, G. Richard, and B. David, "Multiclass feature selection with kernel Gram-matrix-based criteria," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 10, pp. 1611-1623, 2012.
97. K. Neshatian, M. Zhang, and P. Andreae, "A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming," IEEE Trans. Evol. Comput., vol. 16, no. 5, pp. 645-661, 2012.
98. S. Xiang, F. Nie, G. Meng, C. Pan, and C. Zhang, "Discriminative least squares regression for multiclass classification and feature selection," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 11, pp. 1738-1754, 2012.
99. T. Hancock and H. Mamitsuka, "Boosted network classifiers for local feature selection," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 11, pp. 1767-1778, 2012.
100. T. C. Havens, J. C. Bezdek, C. Leckie, L. O. Hall, and M. Palaniswami, "Fuzzy c-means algorithms for very large data," IEEE Trans. Fuzzy Syst., vol. 20, no. 6, pp. 1130-1146, 2012.
101. G. Dudek, "An artificial immune system for classification with local feature selection," IEEE Trans. Evol. Comput., vol. 16, no. 6, pp. 847-860, 2012.
102. J. Chen, Z. Ma, and Y. Liu, "Local coordinates alignment with global preservation for dimensionality reduction," IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 1, pp. 106-117, 2013.
103. D. K. Saxena, J. A. Duro, A. Tiwari, K. Deb, and Q. Zhang, "Objective reduction in many-objective optimization: Linear and nonlinear algorithms," IEEE Trans. Evol. Comput., vol. 17, no. 1, pp. 77-99, 2013.
104. F. E. B. Otero, A. A. Freitas, and C. G. Johnson, "A new sequential covering strategy for inducing classification rules with ant colony algorithms," IEEE Trans. Evol. Comput., vol. 17, no. 1, pp. 64-76, 2013.
105. H. Xie and M. Zhang, "Parent selection pressure auto-tuning for tournament selection in genetic programming," IEEE Trans. Evol. Comput., vol. 17, no. 1, pp. 1-19, 2013.
106. W. Ashlock and S. Datta, "Evolved features for DNA sequence classification and their fitness landscapes," IEEE Trans. Evol. Comput., vol. 17, no. 2, pp. 185-197, 2013.
107. A. Kaban, "Fractional norm regularization: Learning with very few relevant features," IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 6, pp. 953-963, 2013.
108. J. A. Sanz, A. Fernández, H. Bustince, and F. Herrera, "IVTURS: A linguistic fuzzy rulebased classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection," IEEE Trans. Fuzzy Syst., vol. 21, no. 3, pp. 399-411, 2013.
109. U. Bhowan, M. Johnston, M. Zhang, and X. Yao, "Evolving diverse ensembles using genetic programming for classification with unbalanced data," IEEE Trans. Evol. Comput., vol. 17, no. 3, pp. 368-386, 2013.
110. E. Lughofer and O. Buchtala, "Reliable all-pairs evolving fuzzy classifiers," IEEE Trans. Fuzzy Syst., vol. 21, no. 4, pp. 625-641, 2013.
111. M. Tan, I. W. Tsang, and L. Wang, "Minimax sparse logistic regression for very highdimensional feature selection," IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 10, pp. 1609-1622, 2013.
112. H. Izakian, W. Pedrycz, and I. Jamal, "Clustering spatiotemporal data: An augmented fuzzy c-means," IEEE Trans. Fuzzy Syst., vol. 21, no. 5, pp. 855-868, 2013.
113. A. Basak, S. Das, and K. C. Tan, "Multimodal optimization using a biobjective differential evolution algorithm enhanced with mean distance-based selection," IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 666-685, 2013.
114. G. Skolidis and G. Sanguinetti, "Semisupervised multitask learning with Gaussian processes," IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 12, pp. 2101-2112, 2013.
115. K. Subramanian, S. Suresh, and N. Sundararajan, "A metacognitive neuro-fuzzy inference system (McFIS) for sequential classification problems," IEEE Trans. Fuzzy Syst., vol. 21, no. 6, pp. 1080-1095, 2013.
116. W. Dong, T. Chen, P. Tino, and X. Yao, "Scaling up estimation of distribution algorithms for continuous optimization," IEEE Trans. Evol. Comput., vol. 17, no. 6, pp. 797-822, 2013.
117. H. Someya, "Striking a mean-and parent-centric balance in real-valued crossover operators," IEEE Trans. Evol. Comput., vol. 17, no. 6, pp. 737-754, 2013.
118. J. J. Thomas and K. A. Cook, Illuminating the Path: The Research and Development Agenda for Visual Analytics, National Visualization and Analytics Center, 2005.
119. D. L. Donoho, "High-dimensional data analysis: The curses and blessings of dimensionality," in Proc. 21st Century American Mathematical Society Conf. Math Challenges, 2000.
120. I. Arel, D. C. Rose, and T. P. Karnowski, "Deep machine learning-A new frontier in artificial intelligence research," IEEE Comput. Intell. Mag., vol. 5, no. 4, pp. 13-18, Nov. 2010.
121. X. Chen, Y.-S. Ong, M.-H. Lim, and K. C. Tan, "A multi-facet survey on memetic computation," IEEE Trans. Evol. Comput., vol. 15, no. 5, pp. 591-607, Oct. 2011.
122. C. King and D. A. Pendlebury, "Research Fronts 2013," Tech. Rep., Apr. 2013.
123. R. Meuth, M.-H. Lim, Y.-S. Ong, and D. C. Wunsch, "A proposition on memes and meta-memes in computing for higher-order learning," Memetic Compt., vol. 1, no. 2, pp. 85-100, 2009.