D2TS: a dual diversity tree selection approach to pruning of random forests

[1]  Amine Chikh,et al.  A new correlation-based approach for ensemble selection in random forests , 2021, Int. J. Intell. Comput. Cybern..

[2]  Mohamed Medhat Gaber,et al.  eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning , 2020, Big Data Cogn. Comput..

[3]  Mohamed Medhat Gaber,et al.  CLUB-DRF: A Clustering Approach to Extreme Pruning of Random Forests , 2015, SGAI Conf..

[4]  Mohamed Medhat Gaber,et al.  Random forests: from early developments to recent advancements , 2014 .

[5]  Graham J. Williams,et al.  Data Mining with Rattle and R , 2013 .

[6]  Mohamed Medhat Gaber,et al.  GARF: Towards Self-optimised Random Forests , 2012, ICONIP.

[7]  John H. Maindonald,et al.  Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery by Graham Williams , 2012 .

[8]  Graham J. Williams Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery , 2011 .

[9]  Laurent Heutte,et al.  A Study of Strength and Correlation in Random Forests , 2010, ICIC.

[10]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[11]  Laurent Heutte,et al.  On the selection of decision trees in Random Forests , 2009, 2009 International Joint Conference on Neural Networks.

[12]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[13]  Guoyin Wang,et al.  An Approach for Selective Ensemble Feature Selection Based on Rough Set Theory , 2007, RSKT.

[14]  William Nick Street,et al.  Ensemble Pruning Via Semi-definite Programming , 2006, J. Mach. Learn. Res..

[15]  Mykola Pechenizkiy,et al.  Dynamic Integration with Random Forests , 2006, ECML.

[16]  Marko Robnik-Sikonja,et al.  Improving Random Forests , 2004, ECML.

[17]  Rich Caruana,et al.  Ensemble selection from libraries of models , 2004, ICML.

[18]  Alain Hertz,et al.  A survey of local search methods for graph coloring , 2004, Comput. Oper. Res..

[19]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[20]  Tom Heskes,et al.  Clustering ensembles of neural network models , 2003, Neural Networks.

[21]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[22]  Zoran Obradovic,et al.  Effective pruning of neural network classifier ensembles , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[23]  Olivier Debeir,et al.  Limiting the Number of Trees in Random Forests , 2001, Multiple Classifier Systems.

[24]  Fabio Roli,et al.  Design of effective multiple classifier systems by clustering of classifiers , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[25]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[26]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Thomas G. Dietterich,et al.  Pruning Adaptive Boosting , 1997, ICML.

[28]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[29]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[30]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[31]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[32]  Simon Foster,et al.  Optics , 1981, Arch. Formal Proofs.

[33]  D. D. Humphrey Some Adjustments in Census Data on Unemployment , 1937 .

[34]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[35]  J. Hofbauer,et al.  Evolutionary game dynamics , 2011 .

[36]  A. Karegowda,et al.  COMPARATIVE STUDY OF ATTRIBUTE SELECTION USING GAIN RATIO AND CORRELATION BASED FEATURE SELECTION , 2010 .

[37]  Grigorios Tsoumakas,et al.  An Ensemble Pruning Primer , 2009, Applications of Supervised and Unsupervised Ensemble Methods.

[38]  Heping Zhang,et al.  Search for the smallest random forest. , 2009, Statistics and its interface.

[39]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[40]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[41]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[42]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

[43]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .