Rule-Based Classification
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Xiaoli Li | Bing Liu | X. Li | Bing-Rong Liu
[1] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[2] Yoram Singer,et al. Using and combining predictors that specialize , 1997, STOC '97.
[3] Pat Langley,et al. Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.
[4] Xiaoli Li,et al. Learning to Classify Texts Using Positive and Unlabeled Data , 2003, IJCAI.
[5] See-Kiong Ng,et al. Learning to Identify Unexpected Instances in the Test Set , 2007, IJCAI.
[6] Roberto J. Bayardo,et al. Mining the most interesting rules , 1999, KDD '99.
[7] Sholom M. Weiss,et al. Optimized rule induction , 1993, IEEE Expert.
[8] Balaji Padmanabhan,et al. Small is beautiful: discovering the minimal set of unexpected patterns , 2000, KDD '00.
[9] Michael J. Pazzani,et al. A Knowledge-intensive Approach to Learning Relational Concepts , 1991, ML.
[10] Philip S. Yu,et al. Building text classifiers using positive and unlabeled examples , 2003, Third IEEE International Conference on Data Mining.
[11] Heikki Mannila,et al. Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.
[12] Wynne Hsu,et al. Pruning and summarizing the discovered associations , 1999, KDD '99.
[13] Johannes Fürnkranz,et al. Incremental Reduced Error Pruning , 1994, ICML.
[14] Wynne Hsu,et al. Integrating Classification and Association Rule Mining , 1998, KDD.
[15] R. Rivest. Learning Decision Lists , 1987, Machine Learning.
[16] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[17] See-Kiong Ng,et al. Negative Training Data Can be Harmful to Text Classification , 2010, EMNLP.
[18] Anthony K. H. Tung,et al. FARMER: finding interesting rule groups in microarray datasets , 2004, SIGMOD '04.
[19] Philip S. Yu,et al. Text Classification by Labeling Words , 2004, AAAI.
[20] Salvatore J. Stolfo,et al. Adaptive Intrusion Detection: A Data Mining Approach , 2000, Artificial Intelligence Review.
[21] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD '00.
[22] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[23] Philip S. Yu,et al. Positive Unlabeled Learning for Data Stream Classification , 2009, SDM.
[24] David E. Johnson,et al. Maximizing Text-Mining Performance , 1999 .
[25] Anthony K. H. Tung,et al. Mining top-K covering rule groups for gene expression data , 2005, SIGMOD '05.
[26] Aiko M. Hormann,et al. Programs for Machine Learning. Part I , 1962, Inf. Control..
[27] David D. Lewis,et al. Heterogeneous Uncertainty Sampling for Supervised Learning , 1994, ICML.
[28] Nada Lavrac,et al. The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.
[29] George Karypis,et al. Using conjunction of attribute values for classification , 2002, CIKM '02.
[30] Chee Keong Kwoh,et al. Positive-unlabeled learning for disease gene identification , 2012, Bioinform..
[31] Kotagiri Ramamohanarao,et al. DeEPs: A New Instance-Based Lazy Discovery and Classification System , 2004, Machine Learning.
[32] Bing Liu,et al. Identifying comparative sentences in text documents , 2006, SIGIR.
[33] Xiaoli Li,et al. Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery , 2011, BMC Bioinformatics.
[34] Tao Luo,et al. Effective personalization based on association rule discovery from web usage data , 2001, WIDM '01.
[35] Charu C. Aggarwal,et al. XRules: an effective structural classifier for XML data , 2003, KDD '03.
[36] Peter Clark,et al. The CN2 induction algorithm , 2004, Machine Learning.
[37] Mohammed J. Zaki,et al. Mining features for sequence classification , 1999, KDD '99.
[38] Jiawei Han,et al. CPAR: Classification based on Predictive Association Rules , 2003, SDM.
[39] J. J. Rocchio,et al. Relevance feedback in information retrieval , 1971 .
[40] Philip S. Yu,et al. Partially Supervised Classification of Text Documents , 2002, ICML.
[41] Dimitris Meretakis,et al. Extending naïve Bayes classifiers using long itemsets , 1999, KDD '99.
[42] Sergio A. Alvarez,et al. Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.
[43] William W. Cohen. Learning Trees and Rules with Set-Valued Features , 1996, AAAI/IAAI, Vol. 1.
[44] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .
[45] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.
[46] Bing Liu,et al. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.
[47] Ke Wang,et al. Growing decision trees on support-less association rules , 2000, KDD '00.
[48] Gregory Piatetsky-Shapiro,et al. Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.
[49] Abraham Silberschatz,et al. What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..
[50] Jaideep Srivastava,et al. Selecting the right interestingness measure for association patterns , 2002, KDD.
[51] Heikki Mannila,et al. Discovering Generalized Episodes Using Minimal Occurrences , 1996, KDD.
[52] Tong Zhang,et al. A decision-tree-based symbolic rule induction system for text categorization , 2002, IBM Syst. J..
[53] Michael J. Pazzani,et al. An Investigation of Noise-Tolerant Relational Concept Learning Algorithms , 1991, ML.
[54] Ryszard S. Michalski,et al. On the Quasi-Minimal Solution of the General Covering Problem , 1969 .
[55] Pat Morin,et al. Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries , 2005, Discret. Comput. Geom..
[56] Ke Wang,et al. Building Association-Rule Based Sequential Classifiers for Web-Document Prediction , 2004, Data Mining and Knowledge Discovery.
[57] Ron Kohavi,et al. Real world performance of association rule algorithms , 2001, KDD '01.
[58] Arthur B. Maccabe,et al. The architecture of a network level intrusion detection system , 1990 .
[59] William W. Cohen. Learning Rules that Classify E-Mail , 1996 .
[60] Das Amrita,et al. Mining Association Rules between Sets of Items in Large Databases , 2013 .
[61] See-Kiong Ng,et al. Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method , 2006, BMC Bioinformatics.
[62] Weimin Xiao,et al. Rule interestingness analysis using OLAP operations , 2006, KDD '06.
[63] Bogdan E. Popescu,et al. PREDICTIVE LEARNING VIA RULE ENSEMBLES , 2008, 0811.1679.
[64] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[65] Jian Pei,et al. CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[66] Ron Kohavi,et al. Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.
[67] Sholom M. Weiss,et al. Automated learning of decision rules for text categorization , 1994, TOIS.
[68] J. R. Quinlan. Learning Logical Definitions from Relations , 1990 .
[69] Salvatore J. Stolfo,et al. Data Mining Approaches for Intrusion Detection , 1998, USENIX Security Symposium.
[70] Osmar R. Zaïane,et al. Text document categorization by term association , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[71] Yoram Singer,et al. Context-sensitive learning methods for text categorization , 1996, SIGIR '96.