Reference: | Babcock, B., Datar, M., & Motwani, R. (2002). Sampling from a moving window over streaming data. In Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms Society for Industrial and Applied Mathematic, 633-634. Babu, S., & Widom, J. (2001). Continuous queries over data streams. ACM Sigmod Record, 30(3), 109-120. Banerjee, A. (2012). Density-based evolutionary outlier detection. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, 651-652. Barnett, V., & Lewis, T. (1994). Outliers in statistical data (Vol. 3), Wiley, New York. Basu, S., & Meckesheimer, M. (2007). Automatic outlier detection for time series: an application to sensor data. Knowledge and Information Systems, 11(2), 137-154. Bezdek, J. C. (1994). What is computational intelligence? , Computational Intelligence: Imitating Life, 1-12. Bifet, A., Gama, J., Pechenizkiy, M., & Zliobaite, I. (2011). Handling concept drift: Importance, challenges and solutions. PAKDD-2011 Tutorial, Shenzhen, China. Bilge, L., & Dumitras, T. (2012). Before we knew it: an empirical study of zero-day attacks in the real world. In Proceedings of the 2012 ACM conference on Computer and communications security, 833-844. Buschermöhle, A., Schoenke, J., & Brockmann, W. (2012). Uncertainty and Trust Estimation in Incrementally Learning Function Approximation. In Advances on Computational Intelligence (pp. 32-41). Heidelberg: Springer Berlin. Castelo-Fernández, C., De Rezende, P. J., Falcão, A. X., & Papa, J. P. (2010). Improving the accuracy of the optimum-path forest supervised classifier for large datasets. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (pp. 467-475). Heidelberg: Springer Berlin. Chen, C., & Liu, L. M. (1993). Forecasting time series with outliers. Journal of Forecasting, 12(1), 13-35. Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. London: Chamman and Hall. Crawford, K. D., & Wainwright, R. L. (1995). Applying Genetic Algorithms to Outlier Detection. In ICGA, 546-550. Elwell, R., & Polikar, R. (2011). Incremental learning of concept drift in nonstationary environments. Neural Networks, IEEE Transactions on, 22(10), 1517-1531. Ferdousi, Z., & Maeda, A. (2006). Unsupervised outlier detection in time series data. In Data Engineering Workshops, 2006. Proceedings. 22nd International Conference on IEEE, x121-x121. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 46(4), 44. Hawkins, D. M. (1980). Identification of outliers (Vol. 11), London: Chapman and Hall. Hawkins, S., He, H., Williams, G., & Baxter, R. (2002), Outlier detection using replicator neural networks, Warehousing and Knowledge Discovery (pp. 170-180). Berlin Heidelberg: Springer. He, H. (2011). Self-adaptive systems for machine intelligence. John Wiley & Sons. Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126. Huang, S. Y., Yu, F., Tsaih, R. H., & Huang, Y. (2014). Resistant learning on the envelope bulk for identifying anomalous patterns. In Neural Networks (IJCNN), 2014 International Joint Conference on, 3303-3310. Joo, D., Hong, T., & Han, I. (2003). The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors. Expert Systems with Applications, 25(1), 69-75. Krawczyk, B., & Woźniak, M. (2014). One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Computing, 1-14. Lanquillon, C., & Renz, I. (1999). Adaptive information filtering: Detecting changes in text streams. In Proceedings of the eighth international conference on Information and knowledge management, 538-544. Lin, H. C. (2013), ‘An Application of Streaming Data Analysis on TAIEX Futures’, Unpublished Master dissertation, Natioal Cheng-chi University, Taipet , TW. Maggi, F., Robertson, W., Kruegel, C., & Vigna, G. (2009). Protecting a moving target: Addressing web application concept drift. In Recent Advances in Intrusion Detection (pp. 21-40). Springer Berlin Heidelberg. Masud, M. M., Chen, Q., Khan, L., Aggarwal, C., Gao, J., Han, J., & Thuraisingham, B. (2010). Addressing concept-evolution in concept-drifting data streams. In Data Mining (ICDM), 2010 IEEE 10th International Conference on, 929-934. Masud, M. M., Gao, J., Khan, L., Han, J., & Thuraisingham, B. (2011). Classification and novel class detection in concept-drifting data streams under time constraints. Knowledge and Data Engineering, IEEE Transactions on, 23(6), 859-874. Navvab Kashani, M., Aminian, J., Shahhosseini, S., & Farrokhi, M. (2012). Dynamic crude oil fouling prediction in industrial preheaters using optimized ANN based moving window technique. Chemical Engineering Research and Design, 90(7), 938-949. Olson, D. L., & Shi, Y. (2007). Introduction to business data mining. Englewood Cliffs: McGraw-Hill/Irwin. Rousseeuw, P. J., & Van Driessen, K. (2006). Computing LTS regression for large data sets. Data mining and knowledge discovery, 12(1), 29-45. Sendhoff, B., Körner, E., Sporns, O., Ritter, H., & Doya, K. (Eds.). (2009). Creating Brain-Like Intelligence: from basic principles to complex intelligent systems (Vol. 5436). Springer Science & Business Media. Song, J., Takakura, H., & Kwon, Y. (2008). A generalized feature extraction scheme to detect 0-day attacks via IDS alerts. In Applications and the Internet, 2008. SAINT 2008. International Symposium on (pp. 55-61). IEEE. Srinoy, S. (2007). Intrusion detection model based on particle swarm optimization and support vector machine. In Computational Intelligence in Security and Defense Applications, 2007. CISDA 2007. IEEE Symposium on , 186-192. Stanley, K. O. (2003). Learning concept drift with a committee of decision trees. Informe técnico: UT-AI-TR-03-302, Department of Computer Sciences, University of Texas at Austin, USA. Storkey, A. (2009). When training and test sets are different: characterizing learning transfer. Dataset shift in machine learning, 3-28. Sykacek, P. (1997). Equivalent error bars for neural network classifiers trained by Bayesian inference. In ESANN. Tolvi, J. U. S. S. I. (2002). Outliers and Predictability in Monthly Stock Market Index Returns. Liiketaloudellinen aikakauskirja, 369-380. Tsaih, R. H., & Cheng, T. C. (2009). A resistant learning procedure for coping with outliers. Annals of Mathematics and Artificial Intelligence, 57(2), 161-180. Tsay, R. S. (2014). An Introduction to Analysis of Financial Data with R., Wiely. Tsymbal, A. (2004). `The problem of concept drift: definitions and related work`. Computer Science Department, Trinity College Dublin. Wang, H., Fan, W., Yu, P. S., & Han, J. (2003). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 226-235. Warren S. (1983), Cubic Clustering Criterion, SAS Technical Report, A-108, SAS Institute Inc., Wiley. Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1), 69-101. Windham, M. P. (1995). Robustifying model fitting. Journal of the Royal Statistical Society. Series B (Methodological), 599-609. Wrótniak, K., & Woźniak, M. (2013). Combined Bayesian Classifiers Applied to Spam Filtering Problem. In International Joint Conference CISIS’12-ICEUTE´ 12-SOCO´ 12 Special Sessions (pp. 253-260). Springer Berlin Heidelberg. Yoon, K. A., Kwon, O. S., & Bae, D. H. (2007). An approach to outlier detection of software measurement data using the k-means clustering method. In Empirical Software Engineering and Measurement, 2007. ESEM 2007. First International Symposiu, 443-445. Zimek, A., Campello, R. J., & Sander, J. (2014). Ensembles for unsupervised outlier detection: challenges and research questions a position paper. ACM SIGKDD Explorations Newsletter, 15(1), 11-22. |