14.4.3.1 Random Forests Classification

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Random Forest. Create many decision trees for classification.

Dietterich, T.G.,
Random Forests,
MachLearn(40), No. 2, 2000, pp. 139-157. BibRef 0001

Breiman, L.,
Random Forests,
MachLearn(45), No. 1, 2001, pp. 5-32.
DOI Link See also Bagging Predictors. BibRef 0100

Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.,
Investigation of the Random Forest Framework for Classification of Hyperspectral Data,
GeoRS(43), No. 3, March 2005, pp. 492-501.
IEEE Abstract. 0501
BibRef

Kang, Y.[Yousun], Nagahashi, H.[Hiroshi], Sugimoto, A.[Akihiro],
Image Categorization Using Scene-Context Scale Based on Random Forests,
IEICE(E94-D), No. 9, September 2011, pp. 1809-1816.
WWW Link. 1110
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Earlier:
Semantic Segmentation and Object Recognition Using Scene-Context Scale,
PSIVT10(39-45).
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Aoki, K.[Kota], Nagahashi, H.[Hiroshi],
Visual Correspondence Grouping via Local Consistent Neighborhood,
IEICE(E96-D), No. 6, June 2013, pp. 1351-1358.
WWW Link. 1306
Keypoint matches. BibRef

Ye, Y.M.[Yun-Ming], Wu, Q.Y.[Qing-Yao], Huang, J.Z.[Joshua Zhexue], Ng, M.K.[Michael K.], Li, X.T.[Xu-Tao],
Stratified sampling for feature subspace selection in random forests for high dimensional data,
PR(46), No. 3, March 2013, pp. 769-787.
Elsevier DOI 1212
Stratified sampling; High-dimensional data; Classification; Ensemble classifier; Decision trees; Random forests BibRef

Bosch, A.[Anna], Zisserman, A.[Andrew], Muñoz, X.[Xavier],
Scene Classification Using a Hybrid Generative/Discriminative Approach,
PAMI(30), No. 4, April 2008, pp. 712-727.
IEEE DOI 0803
BibRef
Earlier:
Image Classification using Random Forests and Ferns,
ICCV07(1-8).
IEEE DOI 0710
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Earlier:
Scene Classification Via pLSA,
ECCV06(IV: 517-530).
Springer DOI 0608
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Bosch, A.[Anna], Zisserman, A.[Andrew], Munoz, X.[Xavier],
Representing shape with a spatial pyramid kernel,
CIVR07(401-408).
DOI Link 0707
BibRef

Bosch, A.[Anna], Muñoz, X.[Xavier], Marti, J.,
Using Appearance and Context for Outdoor Scene Object Classification,
ICIP05(II: 1218-1221).
IEEE DOI 0512
BibRef

Bosch, A.[Anna], Munoz, X.[Xavier], Oliver, A.[Arnau], Marti, R.[Robert],
Object and Scene Classification: What does a Supervised Approach Provide us?,
ICPR06(I: 773-777).
IEEE DOI 0609
BibRef

Rota Bulò, S.[Samuel], Torsello, A.[Andrea], Pelillo, M.[Marcello],
A game-theoretic approach to partial clique enumeration,
IVC(27), No. 7, 4 June 2009, pp. 911-922.
Elsevier DOI 0904
BibRef
Earlier:
A Continuous-Based Approach for Partial Clique Enumeration,
GbRPR07(61-70).
Springer DOI 0706
Maximal clique enumeration; Maximum clique problem; Evolutionary game theory; Evolutionary stable strategy See also Polynomial-Time Metrics for Attributed Trees. BibRef

Albarelli, A.[Andrea], Rota Bulo, S.[Samuel], Torsello, A.[Andrea], Pelillo, M.[Marcello],
Matching as a non-cooperative game,
ICCV09(1319-1326).
IEEE DOI 0909
See also Fast 3D surface reconstruction by unambiguous compound phase coding. BibRef

Rota Bulo, S., Albarelli, A., Torsello, A., Pelillo, M.,
A hypergraph-based approach to affine parameters estimation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Kontschieder, P.[Peter], Fiterau, M., Criminisi, A., Rota Bulò, S.[Samuel],
Deep Neural Decision Forests,
ICCV15(1467-1475)
IEEE DOI 1602
Award, Marr Prize. Computer vision BibRef

Kontschieder, P.[Peter], Rota Bulò, S.[Samuel], Donoser, M.[Michael], Pelillo, M.[Marcello], Bischof, H.[Horst],
Semantic Image Labelling as a Label Puzzle Game,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Genuer, R.[Robin], Poggi, J.M.[Jean-Michel], Tuleau-Malot, C.[Christine],
Variable selection using random forests,
PRL(31), No. 14, 15 October 2010, pp. 2225-2236.
Elsevier DOI 1003
Random forests; Regression; Classification; Variable importance; Variable selection; High dimensional data BibRef

Whalen, S.[Sean], Peisert, S.[Sean], Bishop, M.[Matt],
Multiclass classification of distributed memory parallel computations,
PRL(34), No. 3, 1 February 2013, pp. 322-329.
Elsevier DOI 1301
Multiclass classification; Bayesian networks; Random forests; Self-organizing maps; High performance computing; Communication patterns BibRef

Désir, C.[Chesner], Bernard, S.[Simon], Petitjean, C.[Caroline], Heutte, L.[Laurent],
One class random forests,
PR(46), No. 12, 2013, pp. 3490-3506.
Elsevier DOI 1308
BibRef
Earlier:
A Random Forest Based Approach for One Class Classification in Medical Imaging,
MLMI12(250-257).
Springer DOI 1211
BibRef
Earlier:
A New Random Forest Method for One-Class Classification,
SSSPR12(282-290).
Springer DOI 1211
One class classification BibRef

Zhang, L.[Le], Suganthan, P.N.[Ponnuthurai Nagaratnam],
Random Forests with ensemble of feature spaces,
PR(47), No. 10, 2014, pp. 3429-3437.
Elsevier DOI 1406
Ensemble BibRef

Hernández-Lobato, D.[Daniel], Martínez-Muñoz, G.[Gonzalo], Suárez, A.[Alberto],
How large should ensembles of classifiers be?,
PR(46), No. 5, May 2013, pp. 1323-1336.
Elsevier DOI 1302
Ensemble learning; Bagging; Random forest; Asymptotic ensemble prediction; Ensemble size BibRef

Quadrianto, N.[Novi], Ghahramani, Z.[Zoubin],
A Very Simple Safe-Bayesian Random Forest,
PAMI(37), No. 6, June 2015, pp. 1297-1303.
IEEE DOI 1506
Bayes methods BibRef

Du, P.J.[Pei-Jun], Samat, A.[Alim], Waske, B.[Björn], Liu, S.C.[Si-Cong], Li, Z.H.[Zhen-Hong],
Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features,
PandRS(105), No. 1, 2015, pp. 38-53.
Elsevier DOI 1506
Polarimetric SAR BibRef

Millard, K.[Koreen], Richardson, M.[Murray],
On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping,
RS(7), No. 7, 2015, pp. 8489.
DOI Link 1506
BibRef

Balzter, H.[Heiko], Cole, B.[Beth], Thiel, C.[Christian], Schmullius, C.[Christiane],
Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests,
RS(7), No. 11, 2015, pp. 14876.
DOI Link 1512
BibRef

Liu, X.[Xiao], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng], Liu, Z.C.[Zi-Cheng], Zhang, L.M.[Lu-Ming], Chen, C.[Chun], Bu, J.J.[Jia-Jun],
Random Forest Construction With Robust Semisupervised Node Splitting,
IP(24), No. 1, January 2015, pp. 471-483.
IEEE DOI 1502
BibRef
Earlier:
Semi-supervised Node Splitting for Random Forest Construction,
CVPR13(492-499)
IEEE DOI 1309
node splitting; random forest; semi-supervised learning See also Attribute-restricted latent topic model for person re-identification. BibRef

Belgiu, M.[Mariana], Dragut, L.[Lucian],
Random forest in remote sensing: A review of applications and future directions,
PandRS(114), No. 1, 2016, pp. 24-31.
Elsevier DOI 1604
Survey, Random Forests. Random forest BibRef


Wallenberg, M.[Marcus], Forssén, P.E.[Per-Erik],
Improving Random Forests by Correlation-Enhancing Projections and Sample-Based Sparse Discriminant Selection,
CRV16(222-227)
IEEE DOI 1612
image classification BibRef

Moradi, M.[Mehdi], Syeda-Mahmood, T.[Tanveer], Hor, S.[Soheil],
Tree-Based Transforms for Privileged Learning,
MLMI16(188-195).
Springer DOI 1611
BibRef

Naji, D., Fakir, M., Bouikhalene, B., Elayachi, R.,
Recognition of 3D Objects Using Heat Diffusion Equations and Random Forests,
CGiV16(161-166)
IEEE DOI 1608
decision trees BibRef

Rodriguez, A.L.[Antonio L.], Sequeira, V.[Vitor],
Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities,
ICCV15(4103-4111)
IEEE DOI 1602
Image recognition BibRef

Modolo, D.[Davide], Vezhnevets, A.[Alexander], Ferrari, V.[Vittorio],
Context Forest for Object Class Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

de Vleeschouwer, C., Legrand, A., Jacques, L., Hebert, M.[Martial],
Mitigating memory requirements for random trees/ferns,
ICIP15(227-231)
IEEE DOI 1512
Random ferns; memory usage; random trees BibRef

Chen, F.[Fan], Liu, Z.C.[Zi-Cheng], Sun, M.T.[Ming-Ting],
Anomaly detection by using random projection forest,
ICIP15(1210-1214)
IEEE DOI 1512
Anomaly Detection; Intelligent Video Surveillance; Random Forest BibRef

Ren, S.Q.[Shao-Qing], Cao, X.D.[Xu-Dong], Wei, Y.C.[Yi-Chen], Sun, J.[Jian],
Global refinement of random forest,
CVPR15(723-730)
IEEE DOI 1510
BibRef

Baumann, F.[Florian], Vogt, K.[Karsten], Ehlers, A.[Arne], Rosenhahn, B.[Bodo],
Probabilistic nodes for modelling classification uncertainty for random forest,
MVA15(510-513)
IEEE DOI 1507
Decision trees BibRef

Villamizar, M., Garrell, A., Sanfeliu, A., Moreno-Noguer, F.,
Multimodal Object Recognition Using Random Clustering Trees,
IbPRIA15(496-504).
Springer DOI 1506
BibRef

Welbl, J.[Johannes],
Casting Random Forests as Artificial Neural Networks (and Profiting from It),
GCPR14(765-771).
Springer DOI 1411
BibRef

Haeusler, R.[Ralf], Nair, R.[Rahul], Kondermann, D.[Daniel],
Ensemble Learning for Confidence Measures in Stereo Vision,
CVPR13(305-312)
IEEE DOI 1309
benchmarking; confidence measure; random forest; stereo BibRef

Wohlhart, P.[Paul], Kostinger, M.[Martin], Donoser, M.[Michael], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Optimizing 1-Nearest Prototype Classifiers,
CVPR13(460-467)
IEEE DOI 1309
Classification BibRef

Schulter, S.[Samuel], Leistner, C.[Christian], Bischof, H.[Horst],
Fast and accurate image upscaling with super-resolution forests,
CVPR15(3791-3799)
IEEE DOI 1510
BibRef

Schulter, S.[Samuel], Leistner, C.[Christian], Wohlhart, P.[Paul], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Accurate Object Detection with Joint Classification-Regression Random Forests,
CVPR14(923-930)
IEEE DOI 1409
BibRef

Wohlhart, P.[Paul], Lepetit, V.[Vincent],
Learning descriptors for object recognition and 3D pose estimation,
CVPR15(3109-3118)
IEEE DOI 1510
BibRef

Schulter, S.[Samuel], Leistner, C.[Christian], Wohlhart, P.[Paul], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Alternating Regression Forests for Object Detection and Pose Estimation,
ICCV13(417-424)
IEEE DOI 1403
Head Pose Estimation; Object Detection; Random Forest; Regression See also On-line Hough Forests. BibRef

Schulter, S.[Samuel], Wohlhart, P.[Paul], Leistner, C.[Christian], Saffari, A.[Amir], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Alternating Decision Forests,
CVPR13(508-515)
IEEE DOI 1309
Boosting; Global Loss; Random Forests BibRef

Straehle, C.[Christoph], Kandemir, M.[Melih], Koethe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Multiple Instance Learning with Response-Optimized Random Forests,
ICPR14(3768-3773)
IEEE DOI 1412
Accuracy BibRef

Straehle, C.[Christoph], Koethe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria,
ICCV13(1849-1856)
IEEE DOI 1403
decision tree BibRef

Straehle, C.[Christoph], Peter, S.[Sven], Köthe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
K-Smallest Spanning Tree Segmentations,
GCPR13(375-384).
Springer DOI 1311
BibRef

Baumann, F.[Florian], Ehlers, A.[Arne], Rosenhahn, B.[Bodo], Liu, W.[Wei],
Sequential Boosting for Learning a Random Forest Classifier,
WACV15(442-447)
IEEE DOI 1503
Accuracy BibRef

Baumann, F.[Florian], Li, F.D.[Fang-Da], Ehlers, A.[Arne], Rosenhahn, B.[Bodo],
Thresholding a Random Forest Classifier,
ISVC14(II: 95-106).
Springer DOI 1501
BibRef

Baumann, F.[Florian], Chen, J.[Jinghui], Vogt, K.[Karsten], Rosenhahn, B.[Bodo],
Improved Threshold Selection by Using Calibrated Probabilities for Random Forest Classifiers,
CRV15(155-160)
IEEE DOI 1507
Accuracy BibRef

Baumann, F.[Florian], Ehlers, A.[Arne], Vogt, K.[Karsten], Rosenhahn, B.[Bodo],
Cascaded Random Forest for Fast Object Detection,
SCIA13(131-142).
Springer DOI 1311
BibRef

Fu, H.[Hao], Zhang, Q.[Qian], Qiu, G.P.[Guo-Ping],
Random Forest for Image Annotation,
ECCV12(VI: 86-99).
Springer DOI 1210
BibRef

Nowozin, S.[Sebastian], Rother, C.[Carsten], Bagon, S.[Shai], Sharp, T.[Toby], Yao, B.P.[Bang-Peng], Kohli, P.[Pushmeet],
Decision tree fields,
ICCV11(1668-1675).
IEEE DOI 1201
Combines and generalizes random forests and conditional random fields. BibRef

Lefort, R.[Riwal], Fablet, R.[Ronan], Boucher, J.M.[Jean-Marc],
Weakly Supervised Classification of Objects in Images Using Soft Random Forests,
ECCV10(IV: 185-198).
Springer DOI 1009
BibRef

Chu, Y.W.[Yu-Wu], Liu, T.L.[Tyng-Luh],
Co-occurrence Random Forests for Object Localization and Classification,
ACCV09(III: 621-632).
Springer DOI 0909
Dealing with weakly labeled data. BibRef

Schwing, A.G.[Alexander G.], Zach, C.[Christopher], Zheng, Y.F.[Ye-Feng], Pollefeys, M.[Marc],
Adaptive random forest: How many 'experts' to ask before making a decision?,
CVPR11(1377-1384).
IEEE DOI 1106
BibRef

Bossard, L.[Lukas], Guillaumin, M.[Matthieu], Van Gool, L.J.[Luc J.],
Food-101: Mining Discriminative Components with Random Forests,
ECCV14(VI: 446-461).
Springer DOI 1408
recognizing pictured dishes. BibRef

Hoo, W.L.[Wai Lam], Kim, T.K.[Tae-Kyun], Pei, Y.[Yuru], Chan, C.S.[Chee Seng],
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,
ICPR14(3434-3439)
IEEE DOI 1412
Accuracy BibRef

Wang, L.[Liang], Wang, Y.Z.[Yi-Zhou], Zhao, D.B.[De-Bin],
Building Emerging Pattern (EP) Random forest for recognition,
ICIP10(1457-1460).
IEEE DOI 1009
BibRef

Wang, A.P.[Ai-Ping], Wan, G.W.[Guo-Wei], Cheng, Z.Q.[Zhi-Quan], Li, S.K.[Si-Kun],
An incremental extremely random forest classifier for online learning and tracking,
ICIP09(1449-1452).
IEEE DOI 0911
BibRef

Perbet, F.[Frank], Stenger, B.[Bjorn], Maki, A.[Atsuto],
Random Forest Clustering and Application to Video Segmentation,
BMVC09(xx-yy).
PDF File. 0909
Large data sets, high-dimensional space. BibRef

Elgawi, O.H.[Osman Hassab], Hasegawa, O.[Osamu],
Online incremental random forests,
ICMV07(102-106).
IEEE DOI 0712
Feature selection technique. BibRef

Elgawi, O.H.[Osman Hassab],
Online random forests based on CorrFS and CorrBE,
Learning08(1-7).
IEEE DOI 0806
BibRef

Prior, M., Windeatt, T.,
Parameter Tuning using the Out-of-Bootstrap Generalisation Error Estimate for Stochastic Discrimination and Random Forests,
ICPR06(II: 498-501).
IEEE DOI 0609
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Self-Organizing Map Classification .


Last update:Jan 22, 2017 at 18:17:25