Abstract
Over the last two decades, information systems have revolutionized with more computer networking, Internets, World Wide Web (www), and Internet of Things (IoT). This resulted in a voluminous increase in both static and dynamic data size, offering a high chance of having potential threats to the global information infrastructure.
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References
Z. Yu, J.J.P. Tsai, Intrusion Detection, A Machine Learning Approach, vol. 3. (Imperial College Press, 2011), ISBN-13: 978-1848164475
M. Abdalla, X. Boyen, C. Chevalier, D. Pointcheval, in Distributed Public-Key Cryptography from Weak Secrets, ed. by S. Jarecki, G. Tsudik Public Key Cryptography—PKC 2009, LNCS 5443, (© Springer, 2009), pp. 139–159
U. Somani, K. Lakhani, M. Mundra, in Implementing Digital Signature with RSA Encryption Algorithm to Enhance the Data Security of Cloud in Cloud Computing. 1st International Conference on Parallel, Distributed and Grid Computing (PDGC—2010) (2010)
D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13(2), 222–232 (1987)
M. Ali Aydin, A. Halim Zaim, K. Gökhan Ceylan, A hybrid intrusion detection system design for computer network security. Comput. Electr. Eng. 35, 517–526 (2009)
D. Anderson, T. Lunt, H. Javitz, A. Tamaru, A. Valdes, Safeguard final report: detecting unusual program behavior using the NIDES statistical component. Technical report, Computer Science Laboratory, SRI International, Menlo Park, CA, 1993
A.H. Sung, S. Mukkamala, in Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks. Proceedings of International Symposium on Applications and the Internet (SAINT 2003) (2003), pp. 209–217
L. Zhou, F. Liu, A swarm-intelligence-based intrusion detection technique. IJCSNS Int. J. Comput. Sci. Netw. Secur. 6(7B) (2006)
S. Owais, V. Snasel, P. Kromer, A. Abraham, Survey: using genetic algorithm approach in intrusion detection systems techniques, in CISIM 2008 (IEEE, 2008), pp. 300–307
P. LaRoche, A. Nur ZincirHeywood, in 802.11 Network Intrusion Detection using Genetic Programming. Proceeding GECCO ‘05 Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation (2005), pp. 170–171
Y. Lin Ying, Y. Zhang, Y.-J. Ou, in The Design and Implementation of Host-based Intrusion Detection System. Third International Symposium on Intelligent Information Technology and Security Informatics (2010)
N. Devarakonda, S. Pamidi, V.V. Kumari, A. Govardhan, Integrated Bayes network and hidden markov model for host based IDS. Int. J. Comput. Appl. 41(20), 45–49 (2012)
J. Shun, H.A. Malki, in Network Intrusion Detection System Using Neural Networks. Fourth International Conference on Natural Computation (2008)
Y.-a. Huang, W. Lee, in A Cooperative Intrusion Detection System for Ad hoc Networks. Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks (2003), pp. 135–147
Y. Wang, H. Yang, X. Wang, R. Zhang, in Distributed Intrusion Detection System Based on Data Fusion Method. Fifth World Congress on Intelligent Control and Automation. WCICA 2004 (2004)
A. Abraham, R. Jain, J. Thomas, S.Y. Han, D-SCIDS: distributed soft computing intrusion detection system. J. Netw. Comput. Appl. 30(1), 81–98 (2007)
R.P. Lippmann, A. Vitae, R.K. Cunningham, Improving intrusion detection performance using keyword selection and neural networks. Comput. Netw. 34(4), 597–603 (2000)
Nsl-kdd data set for network-based intrusion detection systems. http://nsl.cs.unb.ca/KDD/NSL-KDD.html (2009)
G. MeeraGandhi, K. Appavoo, S.K. Srivatsa, Effective network intrusion detection using classifiers decision trees and decision rules. Int. J. Adv. Netw. Appl. 02(03), 686–692 (2010)
D. Gamberger, N. Lavrac, S. Dzeroski, Noise detection and elimination in data preprocessing: experiments in medical domains. Appl. Artif. Intell. 14, 205–223 (2000)
O.P. Rud, Data Mining Cookbook (Wiley Inc., 2001)
S. Chakrabarti, E. Cox, E. Frank, R.H. Güting, J. Han, X. Jiang, M. Kamber, S.S. Lightstone, T.P. Nadeau, R.E. Neapolitan, D. Pyle, M. Refaat, M. Schneider, T.J. Teorey, I.H. Witten, Data Mining: Know It All, (Elsevier, 2005)
E. Rahm, H.H. Do, Data cleaning: problems and current approaches, bulletin of the technical committee on data engineering. IEEE Comput. Soc. 23(4) (2000)
M.L. Lee, H. Lu, T.W. Ling, Y.T. Ko, in Cleansing Data for Mining and Warehousing. Proceedings of 10th DEXA (1999)
Y. Haitovsky, Missing data in regression analysis. J. Roy. Stat. Soc. 30(1) (1968)
S. Oba, M. Sato, I. Takemasa, M. Monden, K. Matsubara, S. Ishii, A Bayesian missing value estimation method. Bioinformatics 19, 2088–2096 (2003)
M. Saar-Tsechansky, F. Provost, Handling missing values when applying classification models. J. Mach. Learn. Res. 8, 1625–1657 (2007)
J. Han, M. Kamber, J. Pei, in Data Mining: Concepts and Techniques”, Third Edition”, The Morgan Kaufmann Series in Data Management Systems, ISBN-10: 0123814790), Morgan Kaufmann Publishers, 2011
Eibe Frank, Mark A. Hall, Ian H. Witten, in Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. The Morgan Kaufmann Series in Data Management Systems (2011)
M.H. Dunham, Data Mining: Introductory and Advanced Topics (Prentice-Hall, 2002). ISBN 0-13-088892-3
D. Hand, H. Mannila, R. Smyth, Principles of Data Mining (MIT, Cambridge, MA, United States, 2001). ISBN 0-262-08290-X
L.T. Jolliffe, Principal Component Analysis (Springer, Berlin, 1986)
W.S. Torgerson, Multidimensional Scaling. Psychometrika 17, 401–419 (1952)
R. Fisher, The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)
R. Jensen, Q. Shen, Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches (Wiley-IEEE Press, 2008)
J.H. Friedman, J.W. Tukey, A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. C 23(9), 881–890 (1974)
J.H. Friedman, W. Stuetzle, Projection pursuit regression. J. Am. Stat. Assoc. 76, 817–823 (1981)
C. Bregler, S. M. Omoundro, in Nonlinear Image Interpolation Using Manifold Learning, ed. by G. Tesauro, D.S. Touretzky, T.K. Leen. Advances in Neural Information Processing Systems, vol. 7 (The MIT Press, 1995), pp. 973–980
M.A. Kramer, Nonlinear principal component analysis using auto associative neural networks. AIChE J. 37(2), 233–243 (1991)
J.B. Tenenbaum, V. de Silva, J.C. Langford, A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Z. Pawlak, Rough Sets—Theoretical Aspects of Reasoning About Data (Kluwer Academic Publishers, Boston, London, Dordrecht, 1991), p. 229
Z. Pawlak, Rough set theory and its applications to data analysis. Cybern. Syst. 29, 661–688 (1998)
R.W. Swiniarski, Rough sets methods in feature reduction and classification. Int. J. Appl. Math. Comput. Sci. 11(3), 565–582 (2001)
Y. Zhao, F. Luo, S.K.M. Wong, Y.Y. Yao, in A General Definition of an Attribute Reduct. Rough Sets and Knowledge Technology, Second International Conference, RSKT 2007, Proceedings, LNAI 4481 (2007), pp. 101–108
C. Liu, Y. Li, Y. Qin, in Research on Anomaly Intrusion Detection Based on Rough Set Attribute Reduction. The 2nd International Conference on Computer Application and System Modeling (Published by Atlantis Press, Paris, France, 2012)
Y.Y. Yao, Y. Zhao, Discernibility matrix simplification for constructing attribute reducts. Inf. Sci. 179(5), 867–882 (2009)
Y. Zhao, Y. Yao, F. Luo, Data analysis based on discernibility and indiscernibility. Inf. Sci. 177, 4959–4976 (2007)
A. Murua, W. Stuetzle, J. Tantrum, S. Sieberts, Model based document classification and clustering. Int. J. Tomogr. Stat. 8(W08), 1–24 (2008)
A. Vimal, S.R. Valluri, K. Karlapalem, in An Experiment with Distance Measures for Clustering. International Conference on Management of Data COMAD 2008, Mumbai, India, 17–19 Dec 2008
V. Torra, in Fuzzy c-means for Fuzzy Hierarchical Clustering. Proceedings of FUZZ ‘05 the 14th IEEE International Conference on Fuzzy Systems, 25–25 May 2005. ISBN: 0-7803-9159-4, pp. 646–651
M.W. Trosset, Representing clusters: K-means clustering, self-organizing maps, and multidimensional scaling. Technical Report 08-03, Department of Statistics, Indiana University, Bloomington, IN, 20 Feb 2008
A. Semana, Z. Abu Bakara, A.M. Sapawia, Centre-based hard and soft clustering approaches for Y-STR data. J. Genet. Genealogy 6(1) (2010)
M. Setnes, Fuzzy relational classifier trained by fuzzy clustering. IEEE Trans. Syst., Man, Cybern. Part B: Cybern. 29(5) (1999)
J. Gomez, D. Dasgupta, in Evolving Fuzzy Classifiers for Intrusion Detection. Proceedings of the 2002 IEEE, Workshop on Information Assurance, United States (2002)
N.R. Pal, J.C. Bezdek, On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3, 370–379 (1995)
D. Dembele, P. Kaster, Fuzzy c-means method for clustering microarray data. Bioinformatics 19(8), 973–980 (2003)
J.C. Bezdek, in Partitioning the Variables for Alternating Optimization of Real-Valued Scalar Fields. Proceedings NAFIPS. 2002 Annual Meeting of the North American Fuzzy Information Processing Society (2002)
S.C.G. Kirkpatrick, C.D. Gelatt, M. Vecchi, Optimization by simulated annealing. Science 220(1983), 49–58 (1983)
Y. Wang, Fuzzy clustering analysis by using genetic algorithm. ICIC Exp. Lett. 2(4), 331–337 (2008)
B.I. Wohlmuth, in Discretization Methods and Iterative Solvers Based on Domain Decomposition. Lecture Notes in Computational Science and Engineering (Springer, 2001)
K. Das, O.P. Vyas, A suitability study of discretization methods for associative classifiers. Int. J. Comput. Appl. 5(10) (2010)
G. Agre, S. Peev, On supervised and unsupervised discretization. Cybern. Inf. Tech. 2(2) (2002)
Q. Zhu, L. Lin, M.-L. Shyu, S.-C. Chen, in Effective Supervised Discretization for Classification Based on Correlation Maximization”, (IRI 2011), pp. 390–395
N. Girard, K. Bertet, M. Visani, in A Local Discretization of Continuous Data for Lattices: Technical Aspects, ed. by A. Napoli, V. Vychodil, (CLA 2011). ISBN 978-2-905267-78-8, pp. 409–412
H. Liu, F. Hussain, C.L. Tan, M. Dash, Discretization: an enabling technique. Data Min. Knowl. Disc. 6, 393–423 (2002)
E. Frank, I.H. Witten, in Making Better Use of Global Discretization. Proceedings of the Sixteenth International Conference on Machine Learning (1999), pp. 115–123
J. Gama, L. Torgo, C. Soares, in Dynamic Discretization of Continuous Attributes. Proceeding IBERAMIA ‘98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence (Springer, London, UK, 1998), pp. 160–169. ISBN:3-540-64992-1
R. Kohavi, M. Sahami, in Error-Based and Entropy-Based Discretization of Continuous Features. Proceedings of the Second International Conference on Knowledge and Data Mining (AAAI Press, Menlo Park, 1996)
J. Gama, C. Pinto, in Discretization from Data Streams: Applications to Histograms and Data Mining. Proceeding of the 2006 ACM Symposium on Applied Computing (2006), pp. 662–667. ISBN:1-59593-108-2, 2006
H. Liu, R. Setiono, in Chi2: Feature Selection and Discretization of Numeric Attributes. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence (1995)
F.E.H. Tay, L. Shen, A modified Chi2 algorithm for discretization. IEEE Trans. Knowl. Data Eng. 14(3) (2002)
R.-P. Li, Z.-O. Wang, in An Entropy-Based Discretization Method for Classification Rules with Inconsistency Checking. Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, 4–5 Nov 2002
R. Jin, Y. Breitbart, C. Muoh, Data discretization unification. J. Knowl. Inf. Syst. Arch. 19(1), 1–29 (2009)
R. Bertelsen, T.R. Martinez, in Extending ID3 Through Discretization of Continuous Inputs, FLAIRS’94 Florida Artificial Intelligence Research Symposium (1994), pp. 122–125
R. Butterworth, D.A. Simovici, G.S. Santos, L. O-M, A greedy algorithm for supervised discretization. J. Biomed. Inform. 37(4), 285–292 (2004)
J.Y. Ching, K.C. Wong Andrew, K.K.C. Chan, Inductive learning from continuous and mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. (1995)
R. Giraldez, J.S., Aguilar-ruiz et al., Discretization oriented to decision rules generation. Front. Artif. Intell. Appl. (2002)
T. Mitchell, Machine Learning (McGraw-Hill, New York, 1997)
J.-S. Roger Jang, C.-T. Sun, E. Mizutani, in Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997). ISBN: 0-13-261066-3
I. Czarnowski, P. Jędrzejowicz, Instance reduction approach to machine learning and multi-database mining. Ann UMCS Inf AI 4, 60–71 (2006)
Y. Li, L. Guo, An active learning based TCM-KNN algorithm for supervised network intrusion detection, in 26th Computers and Security (2007), pp. 459–467
A. Niculescu-Mizil, R. Caruana, in Predicting Good Probabilities with Supervised Learning. Proceedings of 22nd International Conference on Machine Learning (ICML-2005) (ACM Press, New York, NY, USA, 2005), pp. 625–632. ISBN 1-59593-180-52005
G.E. Batista, M.C. Monard, An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17(5–6), 519–533 (2003)
S.B. Kotsiantis, D. Kanellopoulos, P.E. Pintelas, Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1(2) (2006). ISSN 1306-4428
S.B. Kotsiantis, Supervised machine learning: a review of classification techniques. Informatica 31(2007), 249–268 (2007)
T.B.K.A. Bouchard-Côté, J.D.D. Klein, in Painless Unsupervised Learning with Features. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL (Los Angeles, California). 2010 Association for Computational Linguistics (2010), pp. 582–590
Q.V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G.S. Corrado, J. Dean, A.Y. Ng, in Building High-level Features Using Large Scale Unsupervised Learning. Appearing in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012
C. Liu, J. Xie,, J. Xie, J. Xie, in Stochastic Unsupervised Learning on Unlabeled Data. JMLR: Workshop and Conference Proceedings, 2012 Workshop on Unsupervised and Transfer Learning, vol. 27, pp. 111–122
J. Zhuang, J. Wang, X. Lan, in Unsupervised Multiple Kernel Learning. JMLR: Workshop and Conference Proceedings, Asian Conference on Machine Learning, vol. 20, (2011), pp. 129–144
T. Maul, S. Baba, Unsupervised learning in second-order neural networks for motion analysis. Neurocomputing 74, 884–895 (2011)
L.P. Kaelbling, M.L. Littman, A.W. Moore, Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)
R.S. Sutton, A.G. Barto, in Reinforcement Learning: An Introduction (The MIT Press, 1998). ISBN-10: 0262193981
M.A. Wiering, H.P. van Hasselt, Ensemble algorithms in reinforcement learning. IEEE Trans. Syst. Man, Cybern. Part B 38(4), 930–936 (2008)
W. Maydl, B. Sick, in Recurrent and Non-recurrent Dynamic Network Paradigms: A Case Study. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000
C. Rodrigues, P. Gérard, C. Rouveirol, in On and Off-Policy Relational Reinforcement Learning. Late-Breaking Papers of the International Conference on Inductive Logic Programming (2008)
D.R. Lowne, S.J. Roberts, R. Garnett, Sequential non stationary dynamic classification. Pattern Recognit. 43, 897–905 (2010)
S.M. Lee, S.J. Roberts, in Sequential Dynamic Classification Using Latent Variable Models. Advance Access publication on 27 Jan 2010, Published by Oxford University Press on behalf of The British Computer Society, 2010
S.M. Lee, S.J. Roberts, Sequential dynamic classification using latent variable models. Technical report, Technical Report PARG-08-02, University of Oxford, 2008
E. Even-Dar, Y. Mansour, Learning rates for Q-learning. J. Mach. Learn. Res. 5, 1–25 (2003)
R. Dearden, N. Friedman, S. Russell, in Bayesian Q-learning. Fifteenth National Conference on Artificial Intelligence (AAAI) (1998)
Á. Herrero, E. Corchado, Multiagent systems for network intrusion detection: a review, in Computational Intelligence in Security for Information Systems, AISC 63, ed. by Á. Herrero et al. (Springer, Berlin, Heidelberg, 2009) springerlink.com ©, pp. 143–154
W. Lee, in A Data Mining Framework for Constructing Features and Models for Intrusion Detection Systems. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences (Columbia University, 1999)
W. Lee, S.J. Stolfo, K.W. Mok, in A Data Mining Framework for Building Intrusion Detection Models. IEEE Symposium on Security and Privacy (1999), pp. 120–132
A.L. Servin, Multi-Agent Reinforcement Learning for Intrusion Detection, Ph.D. thesis, The University of York, 2009
J.P. Anderson, in Computer Security Threat Monitoring and Surveillance. Technical report (James P. Anderson Co., Fort Washington, PA., 1980)
D.E. Denning, Information Warfare and Security (Addison Wesley Reading, Ma, 1999)
D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13, 222–232 (1987)
S. Axelsson, in Intrusion Detection Systems: A Survey and Taxonomy. Technical Report 99-15 (Chalmers University, 2000)
K. Shafi, An Online and Adaptive Signature-based Approach for Intrusion Detection Using Learning Classifier Systems, PhD thesis, 2008
V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41(3) (2009)
A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, J. Srivastava, in A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. Proceedings of the Third SIAM International Conference on Data Mining, 1–3 May 2003, San Francisco, CA, 2003
J.P. Early, Behavioral Feature Extraction For Network Anomaly Detection. CERIAS Tech Report 2005-55, Doctoral Dissertation, Purdue University West Lafayette, IN, USA (2005). ISBN:0-542-34849-7
R. Sekar, A. Gupta, J. Frullo, T. Hanbhag, A. Tiwari, H. Yang, S. Zhou, Specification-based anomaly detection: a new approach for detecting. Int. J. Netw. Secur. 1(2), 84–102 (2005)
K. Wang, J.J. Parekh, S.J. Stolfo, in Anagram: A Content Anomaly Detector Resistant To Mimicry Attack. Proceedings of the Ninth International Symposium on Recent Advances in Intrusion Detection (RAID), 2006
T. Won, C. Alaettinoglu, in Internet Routing Anomaly Detection and Visualization. Proceedings of International Conference on Dependable Systems and Networks (IEEE, 2005), pp. 172–181
Uci: Machine Learning Repository. http://archive.ics.uci.edu/ml/
R.L. Kruse, A.J. Ryba, in Data Structures and Program Design in C++ (Prentice Hall, 1998). ISBN-13:9780137689958
J.F. Peters (ed.), in Transactions on Rough Sets XI. Lecture Notes in Computer Science/Transactions on Rough Sets (Springer, Berlin, 2010)
J.F. Peters, A. Skowron, C.-C. Chan, J.W. Grzymala-Busse, W.P. Ziarko (eds.), in Transactions on Rough Sets XIII. Lecture Notes in Computer Science/Transactions on Rough Sets (Springer, 2011)
X. Ren, Intrusion detection method using protocol classification and rough set based support vector machine. Comput. Inf. Sci. 2(4), 100–108 (2009)
Y. Caballero, R. Bello, Y. Salgado, M.M. García, A method to edit training set based on rough sets. Int. J. Comput. Intell. Res. 3(3), 219–229. ISSN 0973-1873 (2007)
D. Slezak, G. Wang, M.S. Szczuka, I. Düntsch, Y. Yao, in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. 10th International Conference, RSFDGrC 2005, Regina, Canada, Aug 31—Sept 3, 2005, Proceedings, Part I (Springer, 2005)
D. Deng, H. Huang, Dynamic reduction based on rough sets in incomplete decision systems. Rough Sets Knowl Technol LNCS 4481(2007), 76–83 (2007)
A. Skowron, C. Rauszer, The discernibility matrices and functions in information systems, in Intelligent Decision Support-Handbook of Applications and advances of the Rough Sets Theory, ed. by Slowinski (1991), pp. 331–362
C. Velayutham, K. Thangavel, Unsupervised quick reduct algorithm using rough set theory. J. Electron. Sci. Technol. 9(3) (2011)
S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Netw. 11, 124–136 (2000)
C. Cortes, V.N. Vapnik, Support vector network. Mach. Learn. 20, 273–297 (1995)
V.N. Vapnik, Statistical Learning Theory (Wiley Inc., New York, 1998)
B.E. Boser, I. Guyon, V. Vapnik, in A Training Algorithm for Optimal Margin Classifier. Proceedings of Fifth Annual Conference on Computational Learning Theory (COLT-92), USA, 1992, pp. 144–152
B. Zadrozny, in Learning and Evaluating Classifiers under Sample Selection Bias. International Conference on Machine Learning ICML’04, 2004
R. Jensen, Combining Rough and Fuzzy Sets for Feature Selection. Ph.D. thesis (2005)
S.K. Pal, P. Mitra, Case generation using rough sets with fuzzy representation. IEEE Trans. Knowl. Data Eng. 16(3) (2004)
Richard Jensen, Qiang Shen, Fuzzy-rough sets assisted attribute selection. IEEE Trans. Fuzzy Syst. 15, 73–89 (2007)
R. Jensen, Q. Shen, in Fuzzy-Rough Sets for Descriptive Dimensionality Reduction. Proceedings of the 11th International Conference on Fuzzy Systems (2002), pp. 29–34
M. Yang, S. Chen, X. Yang, in A Novel Approach of Rough Set-Based Attribute Reduction Using Fuzzy Discernibility Matrix. Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 03 (IEEE Computer Society, 2007), pp. 96–101
Hu Qinghua, Yu. Daren, Zongxia Xie, Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27, 414–423 (2006)
R. Jensen, Q. Shen, Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 17(1) (2005)
R. Jensen, Q. Shen, Fuzzy-rough attribute reduction with application to web categorization, fuzzy sets and systems 141(3), 469–485 (2004)
H. Guohua, S. Yuemei, in An Attribute Reduction Method Based on Fuzzy-Rough Sets Theories. First International Workshop on Education Technology and Computer Science (2009)
R. Jensen, Q. Shen, in Rough and Fuzzy Sets for Dimensionality Reduction (2001)
R. Jensen, A. Tuson, Q. Shen, in Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts. Proceedings of FUZZ-IEEE (2010)
R. Jensen, C. Cornelis, in Fuzzy-Rough Instance Selection. Proceedings of FUZZ-IEEE (2010)
E.P. Ephzibah, B. Sarojini, J. Emerald Sheela, A study on the analysis of genetic algorithms with various classification techniques for feature selection. Int. J. Comput. Appl. 8(8) (2010)
M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, Cambridge, MA, 1996)
Y. Rama Devi, P. Venu Gopal, P.S.V.S. Sai Prasad, Fuzzy rough data reduction using SVD. Int. J. Comput. Electr. Eng. 3(3) (2011)
J.R. Anaraki, M. Eftekhari, in Improving Fuzzy-Rough Quick Reduct for Feature Selection. IEEE 19th Iranian Conference on Electrical Engineering (ICEE, 2011), pp. 1–6
S. Kirkpatrik, C. Gelatt, M. Vecchi, Optimization by simulated annealing. Science 220, 671–680 (1983)
S. Bandyopadhyay, Simulated annealing using a reversible jump markov chain monte carlo algorithm for fuzzy clustering. IEEE Trans. Knowl. Data Eng. 17(4) (2005)
X.Y. Wang, G. Whitwell, J.M. Garibaldi, in The Application Of A Simulated Annealing Fuzzy Clustering Algorithm For Cancer Diagnosis. Proceedings of IEEE 4th International Conference on Intelligent Systems Design and Application, Budapest, Hungary, 26–28 Aug 2004, pp. 467–472
N. Sengupta, A. Srivastava, J. Sil, in Chapter 14 Reduction of Data Size in Intrusion Domain Using Modified Simulated Annealing Fuzzy Clustering Algorithm (Springer Science and Business Media LLC, 2013)
C.J.C.H. Watkins, P. Dayan, Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)
S. Manju, M. Punithavalli, An analysis of Q-learning algorithms with strategies of reward function. Int. J. Comput. Sci. Eng. (IJCSE), 3(2) (2011). ISSN: 0975-3397
A.G. Barto, S. Mahadevan, Recent advances in hierarchical reinforcement learning. Discrete Event Dyn. Syst. 13, 341–379 (2003)
T.G. Dietterich, Hierarchical reinforcement learning with the maxq value function decomposition. J. Artif. Intell. Res. 13, 227–303 (2000)
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Sengupta, N., Sil, J. (2020). Introduction. In: Intrusion Detection. Cognitive Intelligence and Robotics. Springer, Singapore. https://doi.org/10.1007/978-981-15-2716-6_1
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