Concept drift detection via competence models

Gao, 2007, On appropriate assumptions to mine data streams: analysis and practice, 143 Zhang, 2008, Categorizing and mining concept drifting data streams, 812 Ouyang, 2009, Mining concept-drifting and noisy data streams using ensemble classifiers, 360 Widmer, 1996, Learning in the presence of concept drift and hidden contexts, Mach. Learn., 23, 69, 10.1007/BF00116900 Hulten, 2001, Mining time-changing data streams, 97 Cohen, 2008, Info-fuzzy algorithms for mining dynamic data streams, Appl. Soft Comput., 8, 1283, 10.1016/j.asoc.2007.11.003 Wei, 2002, Turning telecommunications call details to churn prediction: a data mining approach, Expert Syst. Appl., 23, 103, 10.1016/S0957-4174(02)00030-1 Widmer, 1993, Effective learning in dynamic environments by explicit context tracking, 227 Stanley, 2003 Forman, 2006, Tackling concept drift by temporal inductive transfer, 252 Tsymbal, 2008, Dynamic integration of classifiers for handling concept drift, Inf. Fusion, 9, 56, 10.1016/j.inffus.2006.11.002 Maloof, 2004, Incremental learning with partial instance memory, Artif. Intell., 154, 95, 10.1016/j.artint.2003.04.001 Street, 2001, A streaming ensemble algorithm (SEA) for large-scale classification, 377 Wang, 2003, Mining concept-drifting data streams using ensemble classifiers, 226 Last, 2002, Online classification of nonstationary data streams, Intell. Data Anal., 6, 129, 10.3233/IDA-2002-6203 Hsiao, 2008, An incremental cluster-based approach to spam filtering, Expert Syst. Appl., 34, 1599, 10.1016/j.eswa.2007.01.018 Klinkenberg, 2004, Learning drifting concepts: Example selection vs. example weighting, Intell. Data Anal., 8, 281, 10.3233/IDA-2004-8305 Delany, 2005, A case-based technique for tracking concept drift in spam filtering, Knowl.-Based Syst., 18, 187, 10.1016/j.knosys.2004.10.002 Delany, 2004, An analysis of case-base editing in a spam filtering system, 128 Aha, 1991, Instance-based learning algorithms, Mach. Learn., 6, 37, 10.1007/BF00153759 Cunningham, 2003, A case-based approach to spam filtering that can track concept drift Tsymbal, 2004 Salganicoff, 1997, Tolerating concept and sampling shift in lazy learning using prediction error context switching, Artif. Intell. Rev., 11, 133, 10.1023/A:1006515405170 Elwell, 2011, Incremental learning of concept drift in nonstationary environments, IEEE Trans. Neural Netw., 22, 1517, 10.1109/TNN.2011.2160459 Richter, 1998, Introduction, 1 Smyth, 1995, Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems, 377 Zhu, 1999, Remembering to add: Competence-preserving case-addition policies for case-base maintenance, 234 Smyth, 2001, Competence models and the maintenance problem, Comput. Intell., 17, 235, 10.1111/0824-7935.00142 Massie, 2006, Complexity profiling for informed case-base editing, 325 Craw, 2007, Informed case base maintenance: a complexity profiling approach, 1618 Pan, 2007, Mining competent case bases for case-based reasoning, Artif. Intell., 171, 1039, 10.1016/j.artint.2007.04.018 Wilson, 2001, Maintaining case-based reasoners: Dimensions and directions, Comput. Intell., 17, 196, 10.1111/0824-7935.00140 Lu, 2010, Detecting Change via Competence Model, 201 Žliobaitė, 2010 Kifer, 2004, Detecting change in data streams, vol. 30, 180 Dasu, 2006, An information-theoretic approach to detecting changes in multi-dimensional data streams, 1 Patist, 2007, Optimal window change detection, 557 Bifet, 2007, Learning from time-changing data with adaptive windowing, 443 Gama, 2004, Learning with drift detection, 286 Baena-García, 2006, Early drift detection method, 77 Yasumura, 2007, Quick adaptation to changing concepts by sensitive detection, 855 Li, 2009, Concept drifting detection on noisy streaming data in random ensemble decision trees, 236 Su, 2008, Modeling concept drift from the perspective of classifiers, 1055 Wilcoxon, 1945, Individual comparisons by ranking methods, Biom. Bull., 1, 80, 10.2307/3001968 Kolmogoroff, 1941, Confidence limits for an unknown distribution function, Ann. Math. Stat., 12, 461, 10.1214/aoms/1177731684 Smirnov, 1944, Approximate laws of distribution of random variables from empirical data, Usp. Mat. Nauk, 10, 179 Efron, 1993 Nishida, 2007, Detecting concept drift using statistical testing, 264 Freund, 2007, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 55, 119, 10.1006/jcss.1997.1504 Welch, 1995 Englund, 2005, A hybrid approach to outlier detection in the offset lithographic printing process, Eng. Appl. Artif. Intell., 18, 759, 10.1016/j.engappai.2005.01.008 Angiulli, 2008, Outlier detection using default reasoning, Artif. Intell., 172, 1837, 10.1016/j.artint.2008.07.004 Dries, 2009, Adaptive concept drift detection, Stat. Anal. Data Min., 2, 311, 10.1002/sam.10054 Massie, 2005, What is CBR competence?, BCS-SGAI Expert Update, 8, 7 Smyth, 1998, Modelling the competence of case-bases, 208 McKenna, 2001, Competence-guided case discovery, 97 Brighton, 2002, Advances in instance selection for instance-based learning algorithms, Data Min. Knowl. Discov., 6, 153, 10.1023/A:1014043630878 Koehler, 2009, On the assessment of Monte Carlo error in simulation-based statistical analyses, Am. Stat., 63, 155, 10.1198/tast.2009.0030 Berry, 1983, Moment approximations as an alternative to the F test in analysis of variance, Br. J. Math. Stat. Psychol., 36, 202, 10.1111/j.2044-8317.1983.tb01125.x Opdyke, 2004, Fast permutation tests that maximize power under conventional Monte Carlo sampling for pairwise and multiple comparisons, J. Mod. Appl. Stat. Methods, 2, 27, 10.22237/jmasm/1051747500 Mardia, 1970 Smyth, 2000, An efficient and effective procedure for updating a competence model for case-based reasoners, 357 Gee, 2003, Using latent semantic indexing to filter spam, 460