Random Forests with ensemble of feature spaces

Pattern Recognition

Volumn 47, Issue 10, 2014, Pages 3429-3437

  Zhang, Le ^a   Suganthan, Ponnuthurai Nagaratnam ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Classification; Diversity; Ensemble; Rotation; Transformation

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); FORESTRY; RESEARCH; ROTATION;

DIVERSITY; ENSEMBLE; OVERALL ACCURACIES; POST PROCESSING; RESEARCH FIELDS; ROTATION METHODS; TRAINING PROCESS; TRANSFORMATION;

DECISION TREES;

EID: 84902362182     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.04.001     Document Type: Article

References (47)

1. T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin The elements of statistical learning data mining, inference and prediction Math. Intell. 27 2 2005 83 85
2. T. Dietterich, Ensemble methods in machine learning, in: Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, Springer Berlin Heidelberg, 2000, pp. 1-15.
3. L.I. Kuncheva Combining pattern classifiers methods and algorithms IEEE Trans. Neural Netw. 18 3 2007 964 (L.I. Kuncheva, 2004) [Book Review]
4. E. Bauer, and R. Kohavi An empirical comparison of voting classification algorithms bagging, boosting, and variants Mach. Learn. 36 1-2 1999 105 139
5. L. Breiman Bagging predictors Mach. Learn. 24 2 1996 123 140
6. T. Ho The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. Mach. Intell. 20 8 1998 832 844
7. M. Gönen, and E. AlpaydIn Multiple kernel learning algorithms J. Mach. Learn. Res. 12 2011 2211 2268
8. J. Rodriguez, L. Kuncheva, and C. Alonso Rotation forest a new classifier ensemble method IEEE Trans. Pattern Anal. Mach. Intell 28 10 2006 1619 1630
9. M. Skurichina, and R. Duin The role of combining rules in bagging and boosting Adv. Pattern Recognit. 2000 631 640
10. S. Mao, L. Jiao, L. Xiong, and S. Gou Greedy optimization classifiers ensemble based on diversity Pattern Recognit. 44 6 2011 1245 1261
11. G. Martinez-Muoz, D. Hernández-Lobato, and A. Suárez An analysis of ensemble pruning techniques based on ordered aggregation IEEE Trans. Pattern Anal. Mach. Intell. 31 2 2009 245 259
12. S. Gutta, H. Wechsler, Face recognition using hybrid classifier systems, in: IEEE International Conference on Neural Networks, vol. 2, IEEE, 1996, pp. 1017-1022.
13. T. Hothorn, and B. Lausen Bundling classifiers by bagging trees Comput. Stat. Data Anal. 49 4 2005 1068 1078
14. C. Croux, K. Joossens, and A. Lemmens Trimmed bagging Comput. Stat. Data Anal. 52 1 2007 362 368
15. H. Jiang, Y. Deng, H. Chen, L. Tao, Q. Sha, J. Chen, C. Tsai, and S. Zhang Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes BMC Bioinf. 5 1 2004 81
16. K.-Q. Shen, C.-J. Ong, X.-P. Li, Z. Hui, and E. Wilder-Smith A feature selection method for multilevel mental fatigue eeg classification IEEE Trans. Biomed. Eng. 54 7 2007 1231 1237
17. V. Svetnik, A. Liaw, C. Tong, J. Culberson, R. Sheridan, and B. Feuston Random forest a classification and regression tool for compound classification and qsar modeling J. Chem. Inf. Comput. Sci. 43 6 2003 1947 1958
18. M. Pal Random forest classifier for remote sensing classification Int. J. Remote Sens. 26 1 2005 217 222
19. L. Breiman, J. Friedman, C. Stone, and R. Olshen Classification and Regression Trees 1984 Chapman & Hall/CRC, CRC Press New York
20. B. Menze, W. Petrich, and F. Hamprecht Multivariate feature selection and hierarchical classification for infrared spectroscopy serum-based detection of bovine spongiform encephalopathy Anal. Bioanal. Chem. 387 5 2007 1801 1807
21. Y. Saeys, T. Abeel, and Y. Van de Peer Robust feature selection using ensemble feature selection techniques Mach. Learn. Knowl. Discov. Databases 2008 313 325
22. B. Menze, B. Kelm, R. Masuch, U. Himmelreich, P. Bachert, W. Petrich, and F. Hamprecht A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data BMC Bioinf. 10 1 2009 213
23. E. Tuv, A. Borisov, G. Runger, and K. Torkkola Feature selection with ensembles, artificial variables, and redundancy elimination J. Mach. Learn. Res. 10 2009 1341 1366
24. X. Su, C. Tsai, H. Wang, D. Nickerson, and B. Li Subgroup analysis via recursive partitioning J. Mach. Learn. Res. 10 2009 141 158
25. K. Kandaswamy, K. Chou, T. Martinetz, S. Möller, P. Suganthan, S. Sridharan, and G. Pugalenthi Afp-pred a random forest approach for predicting antifreeze proteins from sequence-derived properties J. Theor. Biol. 270 1 2011 56 62
26. M. Robnik-Šikonja Improving random forests Mach. Learn.: ECML 2004 2004 359 370
27. B. Menze, B. Kelm, D. Splitthoff, U. Koethe, and F. Hamprecht On oblique random forests Mach. Learn. Knowl. Discov. Databases 2011 453 469
28. Ye Yunming et al. Stratified sampling for feature subspace selection in random forests for high dimensional data Pattern Recogn. 46 3 2013 769 787
29. S. Wold, K. Esbensen, and P. Geladi Principal component analysis Chemom. Intell. Lab. Syst. 2 1 1987 37 52
30. D. Zhang, X. Jing, J. Yang, Linear discriminant analysis, in: Biometric Image Discrimination Technologies: Computational Intelligence and Its Applications Series, IgI Global, Hershey, Pennsylvania, USA, 2006, pp. 41-64.
31. T.D. Lemmond, A.O. Hatch, B.Y. Chen, D.A. Knapp, L.J. Hiller, M.J. Mugge, W.G. Hanley, Discriminant random forests, in: Proceedings of the 2008 International Conference on Data Mining, 2008.
32. X. Jiang Linear subspace learning-based dimensionality reduction IEEE Signal Process. Mag. 28 2 2011 16 26
33. K.W. De Bock, and D.V.D. Poel An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction Exp. Syst. Appl. 38 10 2011 12293 12301
34. L.I. Kuncheva, and J.J. Rodríguez An experimental study on rotation forest ensembles Multiple Classifier Systems 2007 Springer pp. 459-468
35. D. Foley, and J. Sammon Jr. An optimal set of discriminant vectors IEEE Trans. Comput. 100 3 1975 281 289
36. J. Kittler, and P. Young A new approach to feature selection based on the Karhunen-Loeve expansion Pattern Recognit. 5 4 1973 335 352
37. S. Kolenikov, and G. Angeles The Use of Discrete Data in PCA: Theory, Simulations, and Applications to Socioeconomic Indices, Carolina Population Center 2004 University of North Carolina Chapel Hill
38. H. Liu, H. Han, J. Li, and L. Wong An in-silico method for prediction of polyadenylation signals in human sequences Genome Inf. Ser. 2003 84 93
39. J. Li, H. Liu, Kent Ridge Bio-medical Data Set Repository, Institute for Infocomm Research, 〈 http://sdmc.lit.org.sg/GEDatasets/Datasets.html 〉.
40. G. Pugalenthi, K.K. Kandaswamy, P. Suganthan, R. Sowdhamini, T. Martinetz, and P.R. Kolatkar Smpred a support vector machine approach to identify structural motifs in protein structure without using evolutionary information J. Biomol. Struct. Dyn. 28 3 2010 405 414
41. F.S. Samaria, A.C. Harter, Parameterisation of a stochastic model for human face identification, in: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, 1994, IEEE, pp. 138-142.
42. T. Sim, S. Baker, M. Bsat, The cmu pose, illumination, and expression (pie) database, in: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002, IEEE, pp. 46-51.
43. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. fisherfaces recognition using class specific linear projection IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 711 720
44. A. Georghiades, P. Belhumeur, and D. Kriegman From few to many illumination cone models for face recognition under variable lighting and pose IEEE Trans. Pattern Anal. Mach. Intell. 23 6 2001 643 660
45. C. Blake, C. Merz, UCI Repository of Machine Learning Databases.
46. M. Legendre, and D. Gautheret Sequence determinants in human polyadenylation site selection BMC Genomics 4 1 2003 7
47. D. Margineantu, T. Dietterich, Pruning adaptive boosting, in: International Workshop Conference on Machine Learning, Citeseer, 1997, pp. 211-218.