A Two-Stage Machine learning approach for temporally-robust text classification
暂无分享,去创建一个
Wagner Meira | Marcos André Gonçalves | Fernando Mourão | Leonardo C. da Rocha | Felipe Viegas | Thiago Salles | Felipe Viegas | L. Rocha | Fernando Mourão | Thiago Salles | Wagner Meira Jr
[1] Gisele L. Pappa,et al. Temporally-aware algorithms for document classification , 2010, SIGIR '10.
[2] Ivan Koychev,et al. Gradual Forgetting for Adaptation to Concept Drift , 2000 .
[3] Yoram Singer,et al. Context-sensitive learning methods for text categorization , 1996, SIGIR '96.
[4] P. John Clarkson,et al. Web-Based Knowledge Management for Distributed Design , 2000, IEEE Intell. Syst..
[5] Alexey Tsymbal,et al. The problem of concept drift: definitions and related work , 2004 .
[6] Gisele L. Pappa,et al. Automatic Document Classification Temporally Robust , 2010, J. Inf. Data Manag..
[7] Wagner Meira,et al. Understanding temporal aspects in document classification , 2008, WSDM '08.
[8] Ralf Klinkenberg,et al. Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..
[9] Stefan Rüping,et al. Concept Drift and the Importance of Example , 2003, Text Mining.
[10] Ricard Gavaldà,et al. Kalman Filters and Adaptive Windows for Learning in Data Streams , 2006, Discovery Science.
[11] P. Royston. Tests for Departure from Normality , 1992 .
[12] Gerhard Widmer,et al. Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.
[13] Charu C. Aggarwal,et al. A Survey of Text Classification Algorithms , 2012, Mining Text Data.
[14] Ludmila I. Kuncheva,et al. On the window size for classification in changing environments , 2009, Intell. Data Anal..
[15] Ralph B. D'Agostino,et al. Tests for Departure from Normality , 1973 .
[16] Wagner Meira,et al. Word co-occurrence features for text classification , 2011, Inf. Syst..
[17] Mohamed S. Kamel,et al. Pairwise optimized Rocchio algorithm for text categorization , 2011, Pattern Recognit. Lett..
[18] E. S. Pearson,et al. Tests for departure from normality. Empirical results for the distributions of b2 and √b1 , 1973 .
[19] Hinrich Schütze,et al. Introduction to information retrieval , 2008 .
[20] George Forman,et al. Tackling concept drift by temporal inductive transfer , 2006, SIGIR.
[21] Philip S. Yu,et al. An ensemble-based approach to fast classification of multi-label data streams , 2011, 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).
[22] Ralf Klinkenberg,et al. Boosting classifiers for drifting concepts , 2007, Intell. Data Anal..
[23] Lior Wolf,et al. In Defense of Word Embedding for Generic Text Representation , 2015, NLDB.
[24] Wagner Meira,et al. Temporal contexts: Effective text classification in evolving document collections , 2013, Inf. Syst..
[25] Xiaowei Yang,et al. Several SVM Ensemble Methods Integrated with Under-Sampling for Imbalanced Data Learning , 2009, ADMA.
[26] Harry Joe,et al. A remark on algorithm 643: FEXACT: an algorithm for performing Fisher's exact test in r x c contingency tables , 1993, TOMS.
[27] Marcos André Gonçalves,et al. Tackling Temporal Effects in Automatic Document Classification , 2011, J. Inf. Data Manag..
[28] Jussara M. Almeida,et al. A quantitative analysis of the temporal effects on automatic text classification , 2016, J. Assoc. Inf. Sci. Technol..
[29] Songbo Tan,et al. Neighbor-weighted K-nearest neighbor for unbalanced text corpus , 2005, Expert Syst. Appl..
[30] Rey-Long Liu,et al. Incremental context mining for adaptive document classification , 2002, KDD.
[31] Enhong Chen,et al. Exploiting probabilistic topic models to improve text categorization under class imbalance , 2011, Inf. Process. Manag..
[32] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[33] Jie Zhou,et al. Transfer estimation of evolving class priors in data stream classification , 2010, Pattern Recognit..
[34] W. Stahel,et al. Log-normal Distributions across the Sciences: Keys and Clues , 2001 .
[35] Koichiro Yamauchi,et al. Learning, detecting, understanding, and predicting concept changes , 2009, 2009 International Joint Conference on Neural Networks.
[36] Koichiro Yamauchi,et al. Detecting Concept Drift Using Statistical Testing , 2007, Discovery Science.
[37] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[38] Gisele L. Pappa,et al. Estimating the Credibility of Examples in Automatic Document Classification , 2010, J. Inf. Data Manag..
[39] Thorsten Joachims,et al. Detecting Concept Drift with Support Vector Machines , 2000, ICML.
[40] Indre Zliobaite,et al. Combining Time and Space Similarity for Small Size Learning under Concept Drift , 2009, ISMIS.
[41] Byeong Ho Kang,et al. Adaptive Web document classification with MCRDR , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..
[42] Marcus A. Maloof,et al. Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.
[43] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[44] Jie Zhou,et al. Non-stationary data sequence classification using online class priors estimation , 2008, Pattern Recognit..
[45] Marcos André Gonçalves,et al. BROOF: Exploiting Out-of-Bag Errors, Boosting and Random Forests for Effective Automated Classification , 2015, SIGIR.
[46] L. Breiman,et al. Submodel selection and evaluation in regression. The X-random case , 1992 .
[47] Anton Dries,et al. Adaptive concept drift detection , 2009, SDM.
[48] Giandomenico Spezzano,et al. An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).
[49] João Gama,et al. Learning with Drift Detection , 2004, SBIA.
[50] C. Lee Giles,et al. Context and Page Analysis for Improved Web Search , 1998, IEEE Internet Comput..
[51] Svetha Venkatesh,et al. Using multiple windows to track concept drift , 2004, Intell. Data Anal..