Automatic recommendation of classification algorithms based on data set characteristics
暂无分享,去创建一个
[1] Nikolaj Tatti,et al. Distances between Data Sets Based on Summary Statistics , 2007, J. Mach. Learn. Res..
[2] Geoffrey I. Webb,et al. MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.
[3] Carla E. Brodley,et al. Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection , 1993 .
[4] Raymond J. Mooney,et al. Symbolic and neural learning algorithms: An experimental comparison , 1991, Machine Learning.
[5] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD '00.
[6] Victor Ciesielski,et al. Matching Data Mining Algorithm Suitability to Data Characteristics Using a Self-Organizing Map , 2001, HIS.
[7] Ian H. Witten,et al. Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.
[8] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[9] Kate Smith-Miles,et al. Cross-disciplinary perspectives on meta-learning for algorithm selection , 2009, CSUR.
[10] Peter I. Cowling,et al. MCAR: multi-class classification based on association rule , 2005, The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005..
[11] Andrew W. Moore,et al. Internet traffic classification using bayesian analysis techniques , 2005, SIGMETRICS '05.
[12] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[13] Robert P. W. Duin,et al. A note on comparing classifiers , 1996, Pattern Recognit. Lett..
[14] D. Kibler,et al. Instance-based learning algorithms , 2004, Machine Learning.
[15] Tin Kam Ho,et al. Complexity Measures of Supervised Classification Problems , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[16] Kate Smith-Miles,et al. On learning algorithm selection for classification , 2006, Appl. Soft Comput..
[17] João Gama,et al. Characterizing the Applicability of Classification Algorithms Using Meta-Level Learning , 1994, ECML.
[18] Geoffrey I. Webb,et al. Not So Naive Bayes: Aggregating One-Dependence Estimators , 2005, Machine Learning.
[19] Peter Clark,et al. The CN2 Induction Algorithm , 1989, Machine Learning.
[20] Carlos Soares,et al. Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information , 2000, PKDD.
[21] D. Wolpert,et al. No Free Lunch Theorems for Search , 1995 .
[22] J. R. Quinlan. Discovering rules by induction from large collections of examples Intro-ductory readings in expert s , 1979 .
[23] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[24] Jian Pei,et al. CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[25] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[26] KATE A. SMITH,et al. Modelling the relationship between problem characteristics and data mining algorithm performance using neural networks , 2001 .
[27] David W. Aha,et al. Generalizing from Case studies: A Case Study , 1992, ML.
[28] Felix Naumann,et al. Data fusion , 2009, CSUR.
[29] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[30] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[31] Qinbao Song,et al. A Weighted Voting-Based Associative Classification Algorithm , 2010, Comput. J..
[32] J. R. Quinlan,et al. Comparing connectionist and symbolic learning methods , 1994, COLT 1994.
[33] Wynne Hsu,et al. Integrating Classification and Association Rule Mining , 1998, KDD.
[34] João Gama,et al. Characterization of Classification Algorithms , 1995, EPIA.
[35] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[36] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[37] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .
[38] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[39] João Gama,et al. On Data and Algorithms: Understanding Inductive Performance , 2004, Machine Learning.
[40] Michael Stuart,et al. Understanding Robust and Exploratory Data Analysis , 1984 .
[41] Sholom M. Weiss,et al. An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.
[42] Carlos Soares,et al. Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results , 2003, Machine Learning.
[43] Robert C. Holte,et al. Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.
[44] J. Platt. Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .
[45] Tin Kam Ho,et al. Domain of competence of XCS classifier system in complexity measurement space , 2005, IEEE Transactions on Evolutionary Computation.
[46] Robert P. W. Duin,et al. On the nonlinearity of pattern classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[47] Donald Michie,et al. Expert systems in the micro-electronic age , 1979 .
[48] Ling Xu,et al. Ordering Effects in Clustering , 1992, ML.
[49] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[50] Frederick Mosteller,et al. Understanding robust and exploratory data analysis , 1983 .
[51] Huan Liu,et al. Searching for interacting features in subset selection , 2009, Intell. Data Anal..
[52] Lloyd A. Smith,et al. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper , 1999, FLAIRS.