Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives
-
DOIhttp://dx.doi.org/10.21511/ppm.17(4).2019.28
-
Article InfoVolume 17 2019, Issue #4, pp. 340-359
- Cited by
- 1008 Views
-
120 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The term “big data” characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs – volume, velocity, variety, and veracity - to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-to-use distributed, scalable, and fault-tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions.
- Keywords
-
JEL Classification (Paper profile tab)M11, M15, C61, C63
-
References143
-
Tables2
-
Figures7
-
- Figure 1. General feature selection process
- Figure 2. Overall categories of evolutionary computation for feature selection
- Figure 3. A general architecture of cooperative co-evolutionary algorithm
- Figure 4. An outline of cooperative co-evolutionary algorithm
- Figure 5. A typical MapReduce workflow shuffled list
- Figure 6. The basic flowchart of a MapReduce model
- Figure 7. Feature selections techniques based on MapReduce
-
- Table 1. Feature selection techniques based on cooperative co-evolution
- Table 2. Feature selection techniques based on cooperative co-evolution and MapReduce
-
- Aghdam, M. H., Ghasem-Aghaee, N., & Basiri, M. E. (2009). Text feature selection using ant colony optimization. Expert Systems with Applications, 36(3), 6843-6853.
- Ahmad, S. S. S., & Pedrycz, W. (2011). Feature and Instance Selection Via Cooperative PSO. 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2127-2132.
- Ahmed, S., Zhang, M. J., & Peng, L. F. (2013). Enhanced Feature Selection for Biomarker Discovery in LC-MS Data using GP. 2013 Ieee Congress on Evolutionary Computation (Cec), 584-591.
- Ali, M. M., Rattanawiboonsom, V., Hassan, F., & Nedelea, A. M. (2019). Knowledge Management at Higher Educational Institutes in Bangladesh: The case study of self-assessed processes of two educational Institutions. Ecoforum Journal, 8(1).
- Aliferis, C. F., Statnikov, A., & Tsamardinos, I. (2006). Challenges in the Analysis of Mass-Throughput Data: A Technical Commentary from the Statistical Machine Learning Perspective. Cancer Informatics, 2, 117693510600200004.
- Bakanauskienė, I., Bendaravičienė, R., & Barkauskė, L. (2017). Features of Employer Attractiveness on Lithuanian Business Organizations: Employees’ Perceptions. Management of Organizations: Systematic Research, 77(1), 7-23.
- Bakanauskienė, I., Bendaravičienė, R., & Juodelytė, N. (2018). Organizational values in human resource management context: case of Lithuania. Human resources management and ergonomics (HRM&E). Zvolen, Slovakia: Technical university in Zvolen, 12(1), 6-20.
- Balkir, A. S., Foster, I., & Rzhetsky, A. (2011). A distributed look-up architecture for text mining applications using mapreduce. Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis.
- Besalatpour, A. A., Ayoubi, S., Hajabbasi, M. A., Jazi, A. Y., & Gharipour, A. (2014). Feature Selection Using Parallel Genetic Algorithm for the Prediction of Geometric Mean Diameter of Soil Aggregates by Machine Learning Methods. Arid Land Research and Management, 28(4), 383-394.
- Bhattacharya, M., Islam, R., & Abawajy, J. (2016). Evolutionary optimization: A big data perspective. Journal of Network and Computer Applications, 59, 416-426.
- Bikku, T., Rao, N. S., & Akepogu, A. R. (2016). Hadoop based Feature Selection and Decision Making Models on Big Data. Indian Journal of Science and Technology 9(10), 1-6.
- Bolon-Canedo, V., Rego-Fernandez, D., Peteiro-Barral, D., Alonso-Betanzos, A., Guijarro-Berdinas, B., & Sanchez-Marono, N. (2018). On the scalability of feature selection methods on high-dimensional data. Knowledge and Information Systems, 56(2), 395-442.
- Boroujeni, F. R., Stantic, B., & Wang, S. (2017). An Embedded Feature Selection Framework for Hybrid Data. Databases Theory and Applications, 10538, 138-150.
- Brahim, A. B., & Limam, M. (2016). A hybrid feature selection method based on instance learning and cooperative subset search. Pattern Recognition Letters, 69, 28-34.
- Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. Ieee Transactions on Evolutionary Computation, 10(6), 646-657.
- Bucci, A., & Pollack, J. B. (2005). On identifying global optima in cooperative coevolution. GECCO 2005: Genetic and Evolutionary Computation Conference, 1-2, 539-544.
- Cao, X. B., Xu, Y. W., Wei, C. X., & Guo, Y. P. (2011). Feature subset selection based on co-evolution for pedestrian detection. Transactions of the Institute of Measurement and Control, 33(7), 867-879.
- Chalikias, M., Kyriakopoulos, G., Skordoulis, M., & Koniordos, M. (2014). Knowledge Management for Business Processes: Employees’ Recruitment and Human Resources’ Selection: A Combined Literature Review and a Case Study. Cham.
- Chen, Z., Lin, T., Tang, N. J., & Xia, X. (2016). A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine. Scientific Programming.
- Clarke, R., Ressom, H. W., Wang, A., Xuan, J., Liu, M. C., Gehan, E. A., & Wang, Y. (2008). The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nature Reviews Cancer, 8, 37.
- Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1(1), 131-156.
- Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1-2), 155-176.
- Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Commun. ACM, 51(1), 107-113.
- Dean, J., & Ghemawat, S. (2010). MapReduce: A Flexible Data Processing Tool. Communications of the Acm, 53(1), 72-77.
- Deepak, M., Mahesh, G., & Medi, N. K. (2019). Knowledge Management Influence on Safety Management Practices: Evidence from Construction Industry. International Journal of Knowledge Management (IJKM), 15(4), 16-37.
- Derrac, J., Garcia, S., & Herrera, F. (2009). A first study on the use of coevolutionary algorithms for instance and feature selection. Hybrid Artificial Intelligence Systems, 557-564.
- Derrac, J., Garcia, S., & Herrera, F. (2010). IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule. Pattern Recognition, 43(6), 2082-2105.
- Ding, W., Lin, C., Chen, S., Zhang, X., & Hu, B. (2018). Multiagent-consensus-MapReduce-based attribute reduction using co-evolutionary quantum PSO for big data applications. Neurocomputing, 272, 136-153.
- Ding, W., Wang, Jie., & Wang, Jia. (2016). A hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm with robust ensemble Pareto equilibrium. Information Sciences, 342, 153-175.
- Ebrahimpour, M. K., Nezamabadi-Pour, H., & Eftekhari, M. (2018). CCFS: A cooperating coevolution technique for large scale feature selection on microarray datasets. Computational Biology and Chemistry, 73, 171-178.
- El-Alfy, E. M., & Alshammari, M. A. (2016). Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduce. Simulation Modelling Practice and Theory, 64, 18-29.
- Fan, J., & Li, R. (2006). Statistical challenges with high dimensionality: Feature selection in knowledge discovery. 25th International Congress of Mathematicians, ICM 2006. Madrid, Spain.
- Fan, Q., & Yan, X. (2015). Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization. Soft Computing, 19(5), 1363-1391.
- Fan, Q., & Yan, X. (2016). Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. Ieee Transactions on Cybernetics, 46(1), 219-232.
- Ferrucci, F., Salza, P., & Sarro, F. (2017). Using Hadoop MapReduce for Parallel Genetic Algorithms: A Comparison of the Global, Grid and Island Models. Evolutionary Computation, XX(X), 1-33.
- Gao, Z., & Tsay, R. S. (2019). A Structural‐Factor Approach to Modeling High‐Dimensional Time Series and Space‐Time Data. Journal of Time Series Analysis, 40(3), 343-362.
- Ghosh, A., Datta, A., & Ghosh, S. (2013). Self-adaptive differential evolution for feature selection in hyperspectral image data. Applied Soft Computing, 13(4), 1969-1977.
- Goberna, M. A., Jeyakumar, V., Li, G., & Vicente-Pérez, J. (2018). Guaranteeing highly robust weakly efficient solutions for uncertain multi-objective convex programs. European Journal of Operational Research, 270(1), 40-50.
- Gore, S., & Govindaraju, V. (2016). Feature Selection Using Cooperative Game Theory and Relief Algorithm. In A. Skulimowski & J. Kacprzyk (Eds.), Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions (pp. 401-412).
- Grandon, E. E., Ramirez-Correa, P. E., & Luna, J. S. (2019). E-Business Applications Model in Large Companies: An Empirical Validation. Interciencia, 44(4), 210-217.
- Grytten, O. H., & Minde, K. B. (2019). Generational links between entrepreneurship, management and puritanism.
- Guillen, A., Sorjamaa, A., Miche, Y., Lendasse, A., & Rojas, I. (2009). Efficient Parallel Feature Selection for Steganography Problems. In J. Cabestany, F. Sandoval, A. Prieto & J. M. Corchado (Eds.), Bio-Inspired Systems: Computational and Ambient Intelligence. Springer, Berlin, Heidelberg.
- Guo, Y. P., Cao, X. B., Xu, Y. W., & Hong, Q. (2007). Co-evolution based feature selection for pedestrian detection. 2007 IEEE International Conference on Control and Automation. Guangzhou, China.
- Gupta, R. (2016). Marketing Management is a Trust Worthy Paradigm of Corporate Branding. International Journal of Information, Business and Management, 8(1), 46-50.
- Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157-1182.
- Habib, M. M., & Hasan, I. (2019). Supply Chain Management (SCM) – Is it Value Addition towards Academia? IOP Conference Series: Materials Science and Engineering, 528(1), 012090.
- Hadoop Apache. (2018). HDFS Architecture.
- Hancer, E., Xue, B., Zhang, M. J., Karaboga, D., & Akay, B. (2015). A Multi-Objective Artificial Bee Colony Approach to Feature Selection Using Fuzzy Mutual Information. 2015 IEEE Congress on Evolutionary Computation (CEC), 2420-2427.
- Hashem, I. A. T., Anuar, N. B., Gani, A., Yaqoob, I., Xia, F., & Khan, S. U. (2016). MapReduce: Review and open challenges. Scientometrics, 109(1), 389-422.
- He, Q., Cheng, X. H., Zhuang, F. Z., & Shi, Z. Z. (2014). Parallel Feature Selection Using Positive Approximation Based on MapReduce. 11th International Conference on Fuzzy Systems and Knowledge Discovery (Fskd), 397-402.
- Hodge, V. J., O’Keefe, S., & Austin, J. (2016). Hadoop neural network for parallel and distributed feature selection. Neural Networks, 78, 24-35.
- Hoverstad, B. A. (2007). Revisiting the personal satellite assistant: neuroevolution with a modified enforced sub-populations algorithm. Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation.
- Hunt, R., Neshatian, K., & Zhang, M. (2012). A Genetic Programming Approach to Hyper-Heuristic Feature Selection. Berlin, Heidelberg.
- IBM. (2018). IBM big data analytics: insights without limits.
- Illiashenko, S. M., Strielkowski, W., Letunovska, N. Y., Bozhkova, V. V., Prokopenko, O. V., Tielietov, O. S.,…, & Hryshchenko, O. F. (2018). Innovative management: theoretical, methodical, and applied grounds.
- Inotai, A., Brixner, D., Maniadakis, N., Dwiprahasto, I., Kristin, E., Prabowo, A.,…, & Kalo, Z. (2018). Development of multi-criteria decision analysis (MCDA) framework for off-patent pharmaceuticals – an application on improving tender decision making in Indonesia. BMC health services research, 18(1), 1003.
- Islam, A. K. M. T., Jeong, B. S., Bari, A. T. M. G., Lim, C. G., & Jeon, S. H. (2015). MapReduce based parallel gene selection method. Applied Intelligence, 42(2), 147-156.
- Juillé, H., & Pollack, J. B. (1996). Co-evolving Intertwined Spirals. Proceedings of the Fifth Annual Conference on Evolutionary Programming, 461-468.
- Kannan, S. S., & Ramaraj, N. (2010). A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm. Knowledge-Based Systems, 23(6), 580-585.
- Ketcha, A., Johannesson, J., & Bocij, P. (2015). Tacit knowledge acquisition and dissemination in distance learning. European Journal of Open, Distance and E-learning, 18(2).
- Khan, N., & Kakabadse, N. K. (2014). CSR: the co-evolution of grocery multiples in the UK (2005–2010). Social Responsibility Journal, 10(1), 137-160.
- Kim, G., Kim, Y., Lim, H., & Kim, H. (2010). An MLP-based feature subset selection for HIV-1 protease cleavage site analysis. Artificial Intelligence in Medicine, 48(2), 83-89.
- Kira, K., & Rendell, L. A. (1992). A Practical Approach to Feature-Selection. Machine Learning, 92, 249-256.
- Kourid, A., & Batouche, M. (2015). Biomarker Discovery Based on Large-Scale Feature Selection and MapReduce. IFIP International Conference on Computer Science and Its Applications (pp. 81-92).
- Kumar, M., Rath, N. K., Swain, A., & Rath, S. K. (2015). Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor. Procedia Computer Science, 54, 301-310.
- Lane, M. C., Xue, B., Liu, I., & Zhang, M. (2013). Particle Swarm Optimisation and Statistical Clustering for Feature Selection. In Australasian Joint Conference on Artificial Intelligence, 214-220.
- Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety.
- Levner, I. (2005). Feature selection and nearest centroid classification for protein mass spectrometry. BMC Bioinformatics, 6, 68.
- Li, Y. M., Zhang, S. J., & Zeng, X. P. (2009). Research of multi-population agent genetic algorithm for feature selection. Expert Systems with Applications, 36(9), 11570-11581.
- Lin, F., Liang, D., Yeh, C.-C., & Huang, J.-C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41(5), 2472-2483.
- Lin, J. Y., Ke, H. R., Chien, B. C., & Yang, W. P. (2008). Classifier design with feature selection and feature extraction using layered genetic programming. Expert Systems with Applications, 34(2), 1384-1393.
- Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. Ieee Transactions on Knowledge and Data Engineering, 17(4), 491-502.
- Liu, H., Motoda, H., Setiono, R., & Zhao, Z. (2010). Feature Selection: An Ever Evolving Frontier in Data Mining. Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, 10, 4-13.
- Liu, Y. N., Wang, G., Chen, H. L., Dong, H., Zhu, X. D., & Wang, S. J. (2011). An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, 8(2), 191-200.
- Liu, Y., Tang, F., & Zeng, Z. Y. (2015). Feature Selection Based on Dependency Margin. Ieee Transactions on Cybernetics, 45(6), 1209-1221.
- Luque, G., & Alba, E. (2011). Parallel genetic algorithms: Theory and real world applications. Springer.
- Ma, X., Li, X., Zhang, Q., Tang, K., Liang, Z., Xie, W., & Zhu, Z. (2018). A Survey on Cooperative Co-evolutionary Algorithms. Ieee Transactions on Evolutionary Computation, 1-1.
- Mao, Q., & Tsang, I. W. H. (2013). A Feature Selection Method for Multivariate Performance Measures. Ieee Transactions on Pattern Analysis and Machine Intelligence, 35(9), 2051-2063.
- Marill, T., & Green, D. (1963). On the effectiveness of receptors in recognition systems. Ieee Transactions on Information Theory, 9(1), 11-17.
- Mokshin, V., Saifudinov, I., Sharnin, L., Trusfus, M., & Tutubalin, P. (2018). A parallel genetic algorithm of feature selection for analysis of complex system. Journal of Physics: Conference Series, 1096(1).
- Mortazavi, A., & Moattar, M. H. (2016). Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information. Advances in Bioinformatics, 2016, 1058305.
- Mura, L., Daňová, M., Vavrek, R., & Dubravska, M. (2017). Economic Freedom-Classification of its Level and Impact on the Economic Security. Ad Alta: Journal of Interdisciplinary Research, 7(2), 154-157.
- Omidvar, M. N., Li, X., Mei, Y., & Yao, X. (2014). Cooperative co-evolution with differential grouping for large scale optimization. Ieee Transactions on Evolutionary Computation, 18(3), 378-393.
- Omidvar, M. N., Yang, M., Mei, Y., Li, X. D., & Yao, X. (2017). DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization. Ieee Transactions on Evolutionary Computation, 21(6), 929-942.
- Pagie, L., & Hogeweg, P. (2000). Information integration and red queen dynamics in coevolutionary optimization. Proceedings of the 2000 Congress on Evolutionary Computation, 1-2, 1260-1267.
- Palma-Mendoza, R. J., Rodriguez, D., & de-Marcos, L. (2018). Distributed ReliefF-based feature selection in Spark. Knowledge and Information Systems, 57(1), 1-20.
- Panait, L., Luke, S., & Harrison, J. F. (2006). Archive-based cooperative coevolutionary algorithms. Gecco 2006: Genetic and Evolutionary Computation Conference, 1-2, 345-352.
- Peng, H. C., Long, F. H., & Ding, C. (2005). Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. Ieee Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226-1238.
- Peralta, D., del Rio, S., Ramirez-Gallego, S., Triguero, I., Benitez, J., & Herrera, F. (2015). Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach. Mathematical Problems in Engineering, 2015, 245139.
- Pierreval, H., & Paris, J. (2000). Distributed evolutionary algorithms for simulation optimization. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(1), 15-24.
- Potter, M. A. (1997). The design and analysis of a computational model of cooperative coevolution. George Mason University Fairfax, VA, USA.
- Potter, M. A., & de Jong, K. A. (1994). A Cooperative Coevolutionary Approach to Function Optimization. Parallel Problem Solving from Nature – PPSN III, 249-257.
- Potter, M. A., & de Jong, K. A. (1995). Evolving neural networks with collaborative species. Proceedings of the Summer Computer Simulation Conference, 340-345. The Society for Computer Simulation, San Diego, California.
- Potter, M. A., & de Jong, K. A. (2000). Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents. Evolutionary Computation, 8(1), 1-29.
- Pudil, P., Novovicova, J., & Kittler, J. (1994). Floating Search Methods in Feature-Selection. Pattern Recognition Letters, 15(11), 1119-1125.
- Ramírez-Gallego, S., Mouriño-Talín, H., Martínez-Rego, D., Bolón-Canedo, V., Benítez, J. M., Alonso-Betanzos, A., & Herrera, F. (2018). An Information Theory-Based Feature Selection Framework for Big Data Under Apache Spark. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1441-1453.
- Reggiani, C., Le Borgne, Y. A., & Bontempi, G. (2018). Feature Selection in High-Dimensional Dataset Using MapReduce. Paper presented at the Benelux Conference on Artificial Intelligence (pp. 101-115). Cham: Springer.
- Rentsen, E., Zhou, J., & Teo, K. L. (2016). A global optimization approach to fractional optimal control. Journal of Industrial and Management Optimization (JIMO), 12(1), 73-82.
- Sakinah, S., & Ahmad, S. (2014). Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO. 2014 4th World Congress on Information and Communication Technologies (WICT), 45-50.
- Shelke, K., Jayaraman, S., Ghosh, S., & Valadi, J. (2013). Hybrid Feature Selection and Peptide Binding Affinity Prediction using an EDA based Algorithm. 2013 Ieee Congress on Evolutionary Computation (Cec), 2384-2389.
- Shen, Q., Shi, W., & Kong, W. (2008). Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Computational Biology and Chemistry, 32(1), 53-60.
- Shi, M., & Gao, S. (2017). Reference sharing: a new collaboration model for cooperative coevolution. Journal of Heuristics, 23(1), 1-30.
- Shi, Y. J., Li, R., & Teo, K. L. (2015). Cooperative enclosing control for multiple moving targets by a group of agents. International Journal of Control, 88(1), 80-89.
- Shim, K. (2012). MapReduce algorithms for big data analysis. Proceedings of the Vldb Endowment, 5(12), 2016-2017.
- Singh, S., Kubica, J., Larsen, S., & Sorokina, D. (2009). Parallel Large Scale Feature Selection for Logistic Regression. Proceedings of the 2009 SIAM International Conference on Data Mining (pp. 1172-1183).
- Sinha, A., & Jana, P. K. (2018). A hybrid MapReduce-based k-means clustering using genetic algorithm for distributed datasets. Journal of Supercomputing, 74(4), 1562-1579.
- Sofge, D., De Jong, K., & Schultz, A. (2002). A blended population approach to cooperative coevolution for decomposition of complex problems. CEC’02: Proceedings of the 2002 Congress on Evolutionary Computation, 1-2, 413-418.
- Somorjai, R. L., Dolenko, B., & Baumgartner, R. (2003). Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics, 19(12), 1484-1491.
- Song, A., Yang, Q., Chen, W. N., & Zhang, J. (2016). A Random-Based Dynamic Grouping Strategy for Large Scale Multi-objective Optimization. 2016 IEEE Congress on Evolutionary Computation (CEC), 468-475.
- Soufan, O., Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015). DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm. Plos One, 10(2).
- Stanovov, V., Brester, C., Kolehmainen, M., & Semenkina, O. (2017). Why don’t you use Evolutionary Algorithms in Big Data? IOP Conference Series: Materials Science and Engineering, 173(1), 012020.
- Stoeckel, J., & Fung, G. (2005). SVM Feature Selection for Classification of SPECT Images of Alzheimer’s Disease Using Spatial Information. Proceedings of the Fifth IEEE International Conference on Data Mining.
- Storn, R., & Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.
- Strearns, S. D. (1976). On selecting features for pattern classifiers. Proceedings of the International Conference on Pattern Recognition (ICPR), 71-75.
- Sun, Y., Kirley, M., & Halgamuge, S. K. (2015). Extended differential grouping for large scale global optimization with direct and indirect variable interactions. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation.
- Sun, Y., Kirley, M., & Halgamuge, S. K. (2018). A Recursive Decomposition Method for Large Scale Continuous Optimization. Ieee Transactions on Evolutionary Computation, 22(5), 647-661.
- Sun, Y., Omidvar, M. N., Kirley, M., & Li, X. (2018). Adaptive threshold parameter estimation with recursive differential grouping for problem decomposition. Proceedings of the Genetic and Evolutionary Computation Conference, 889-896. Kyoto, Japan.
- Sun, Z. (2014). Parallel Feature Selection Based on MapReduce. Paper presented at the Computer Engineering and Networking (pp. 299-306). Cham.
- Tan, M. K., Tsang, I. W., & Wang, L. (2013). Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection. Ieee Transactions on Neural Networks and Learning Systems, 24(10), 1609-1622.
- Tan, N. C., Fisher, W. G., Rosenblatt, K. P., & Garner, H. R. (2009). Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery. BMC Bioinformatics, 10(1), 144.
- Taylor, R. C. (2010). An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. BMC Bioinformatics, 11(S1).
- Tian, J., Li, M., & Chen, F. (2010). Dual-population based coevolutionary algorithm for designing RBFNN with feature selection. Expert Systems with Applications, 37(10), 6904-6918.
- Triguero, I., Peralta, D., Bacardit, J., García, S., & Herrera, F. (2015). MRPR: A MapReduce solution for prototype reduction in big data classification. Neurocomputing, 150, 331-345.
- Tseng, M.-L., Wu, K.-J., Lim, M. K., & Wong, W.-P. (2019). Data-driven sustainable supply chain management performance: A hierarchical structure assessment under uncertainties. Journal of Cleaner Production, 227, 760-771.
- Tursunbayeva, A., Bunduchi, R., Franco, M., & Pagliari, C. (2016). Human resource information systems in health care: a systematic evidence review. Journal of the American Medical Informatics Association, 24(3), 633-654.
- Vatolkin, I., Theimer, W., & Rudolph, G. (2009). Design and Comparison of Different Evolution Strategies for Feature Selection and Consolidation in Music Classification. 2009 IEEE Congress on Evolutionary Computation, 1-5, 174-181.
- Vieira, S. M., Sousa, J. M. C., & Runkler, T. A. (2010). Two cooperative ant colonies for feature selection using fuzzy models. Expert Systems with Applications, 37(4), 2714-2723.
- Voyer, J., Dean, M. D., Pickles, C. B., & Robar, C. R. (2018). Leveraging System Dynamics Modeling to Help Understand Humanitarian Food Supply During Disaster Response. Journal of Strategic Innovation & Sustainability, 13(4), 52-70.
- Wang, K. J., Chen, K. H., & Angelia, M. A. (2014). An improved artificial immune recognition system with the opposite sign test for feature selection. Knowledge-Based Systems, 71, 126-145.
- Wang, S., Pedrycz, W., Zhu, Q., & Zhu, W. (2015). Subspace learning for unsupervised feature selection via matrix factorization. Pattern Recognition, 48(1), 10-19.
- Whitney, A. W. (1971). A direct method of nonparametric measurement selection. Ieee Transactions on Computers, 100(9), 1100-1103.
- Wiegand, R. P. (2004). An analysis of cooperative coevolutionary algorithms. George Mason University.
- Wu, M., Liu, K., & Yang, H. (2018). Supply chain production and delivery scheduling based on data mining. Cluster Computing.
- Xue, B., Zhang, M. J., Browne, W. N., & Yao, X. (2016). A Survey on Evolutionary Computation Approaches to Feature Selection. Ieee Transactions on Evolutionary Computation, 20(4), 606-626.
- Yamada, M., Tang, J. L., Lugo-Martinez, J., Hodzic, E., Shrestha, R., Saha, A.,…, & Chang, Y. (2018). Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data. Ieee Transactions on Knowledge and Data Engineering, 30(7), 1352-1365.
- Yang, Z. Y., Tang, K., & Yao, X. (2008a). Large scale evolutionary optimization using cooperative coevolution. Information Sciences, 178(15), 2985-2999.
- Yang, Z. Y., Tang, K., & Yao, X. (2008b). Multilevel Cooperative Coevolution for Large Scale Optimization. 2008 IEEE Congress on Evolutionary Computation, 1-8, 1663-1670.
- Yang, Z. Y., Tang, K., & Yao, X. (2008c). Self-adaptive differential evolution with neighborhood search. Proceedings of the IEEE Congress on Evolutionary Computatio (CEC 2008). Hong Kong, China.
- Yang, Z., Yao, X., & He, J. (2008). Making a Difference to Differential Evolution. In P. Siarry & Z. Michalewicz (Eds.), Advances in Metaheuristics for Hard Optimization (pp. 397-414). Berlin, Heidelberg: Springer Berlin Heidelberg.
- Yee, Y. M., Tan, C. L., & Ramayah, T. (2017). Connect the Silos: Knowledge Management, Absorptive Capacity, Leadership Styles, Organisational Cultures. Paper presented at the International Conference on Intellectual Capital and Knowledge Management and Organisational Learning (pp. 310-315).
- Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: cluster computing with working sets. Proceedings of the 2nd USENIX conference on Hot topics in cloud computing. Boston, MA.
- Zhao, X. H., Li, D. L., Yang, B., Ma, C., Zhu, Y. G., & Chen, H. L. (2014). Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Applied Soft Computing, 24, 585-596.
- Zhou, Z., Chawla, N. V., Jin, Y., & Williams, G. J. (2014). Big data opportunities and challenges: Discussions from data analytics perspectives. Ieee Computational Intelligence Magazine, 9(4), 62-74.