14.4.3 Decision Trees

de Souza, P.[Peter],
Some decision network designs for pattern classification,
PR(15), No. 3, 1982, pp. 193-200.
WWW Version. 0309 BibRef

Tamura, S.[Shinichi],
Clustering based on multiple paths,
PR(15), No. 6, 1982, pp. 477-483.
WWW Version. 0309 BibRef

Kurzynski, M.W.[Marek W.],
The optimal strategy of a tree classifier,
PR(16), No. 1, 1983, pp. 81-87.
WWW Version. 0309 See also On the multistage Bayes classifier. See also On the Identity of Optimal Strategies for Multistage Classifiers. BibRef

Quinlan, J.R.,
Induction of Decision Trees,
MachLearn(1), No. 1, 1986, pp. 81-106. BibRef 8600

Li, X.B.[Xiao-Bo], Dubes, R.C.[Richard C.],
Tree classifier design with a permutation statistic,
PR(19), No. 3, 1986, pp. 229-235.
WWW Version. 0309 BibRef

Shlien, S.[Seymour],
Multiple binary decision tree classifiers,
PR(23), No. 7, 1990, pp. 757-763.
WWW Version. 0401 BibRef

Park, Y.T.[Young-Tae], Sklansky, J.[Jack],
Automated design of linear tree classifiers,
PR(23), No. 12, 1990, pp. 1393-1412.
WWW Version. 0401 BibRef
And:
Fast tree classifiers,
ICPR90(I: 684-686).
WWW Version. 9006 BibRef
Earlier:
Automated design of piecewise-linear classifiers of multiple-class data,
ICPR88(II: 1068-1071).
WWW Version. 8811 BibRef

Brown, D.E.[Donald E.], Corruble, V.[Vincent], Pittard, C.L.[Clarence Louis],
A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems,
PR(26), No. 6, June 1993, pp. 953-961.
WWW Version. 0401 BibRef

Brown, D.E., Pittard, C.L., Park, H.,
Classification Trees with Optimal Multivariate Decision Nodes,
PRL(17), No. 7, June 10 1996, pp. 699-703. 9607 BibRef

Gelfand, S.B., Ravishankar, C.S., and Delp, E.J.,
An Iterative Growing and Pruning Algorithm for Classification Tree Design,
PAMI(13), No. 2, February 1991, pp. 163-174.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9102

Guo, H., Gelfand, S.B.,
Classification trees with neural network feature extraction,
CVPR92(183-188).
IEEE Abstract. IEEE Top Reference. 0403 BibRef

Safavian, S.R.[S. Rasoul], and Landgrebe, D.A.[David A.],
A Survey of Decision Tree Classifier Methodology,
SMC(21), No. 3, May 1991, pp. 660-674.
PDF Version. Survey, Decision Tree. BibRef 9105

Zhou, X.J.[Xiao Jia], and Dillon, T.S.[Tharam S.],
A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction,
PAMI(13), No. 8, August 1991, pp. 834-841.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9108

Draper, B.A., Brodley, C.E., Utgoff, P.E.,
Goal-Directed Classification Using Linear Machine Decision Trees,
PAMI(16), No. 9, September 1994, pp. 888-893.
IEEE Abstract. IEEE Top Reference.
WWW Version.
Postscript Version. BibRef 9409

Sethi, I.K., Yoo, J.H.,
Design Of Multicategory Multifeature Split Decision Trees Using Perceptron Learning,
PR(27), No. 7, July 1994, pp. 939-947.
WWW Version. BibRef 9407

Lovell, B.C., Bradley, A.P.,
The Multiscale Classifier,
PAMI(18), No. 2, February 1996, pp. 124-137.
IEEE Abstract. IEEE Top Reference.
WWW Version. Decision Tree. BibRef 9602

Chaudhuri, D., Chaudhuri, B.B., Murthy, C.A.,
A Data-driven Procedure for Density-Estimation with Some Applications,
PR(29), No. 10, October 1996, pp. 1719-1736.
WWW Version. Probability Density Estimation. Minimal Spanning Tree. BibRef 9610

Esposito, F., Malerba, D., Semeraro, G.,
A Comparative-Analysis of Methods for Pruning Decision Trees,
PAMI(19), No. 5, May 1997, pp. 476-491.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9705See the comment paper also. BibRef

Kay, J.,
A Comparative-Analysis of Methods for Pruning Decision Trees: Comment,
PAMI(19), No. 5, May 1997, pp. 492-493.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9705 BibRef

Malerba, D.[Donato], Esposito, F.[Floriana], Ceci, M.[Michelangelo], Appice, A.[Annalisa],
Top-down induction of model trees with regression and splitting nodes,
PAMI(26), No. 5, May 2004, pp. 612-625.
IEEE Abstract. IEEE Top Reference. 0404Model trees extend regression trees by having multiple regression models. BibRef

Sethi, I.K., Yoo, J.H.,
Structure-Driven Induction of Decision Tree Classifiers Through Neural Learning,
PR(30), No. 11, November 1997, pp. 1893-1904.
WWW Version. 9801 BibRef

Friedl, M.A., Brodley, C.E.,
Decision Tree Classification of Land-Cover from Remotely-Sensed Data,
RSE(61), No. 3, September 1997, pp. 399-409. 9708 BibRef

Jun, B.H., Kim, C.S., Song, H.Y., Kim, J.,
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees,
PAMI(19), No. 12, December 1997, pp. 1371-1375.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9712 BibRef

Chowdhury, N., Murthy, C.A.,
Minimal Spanning Tree-Based Clustering Technique: Relationship with Bayes Classifier,
PR(30), No. 11, November 1997, pp. 1919-1929.
WWW Version. 9801 Minimal Spanning Tree. BibRef

Lam, C.P., West, G.A.W., Caelli, T.M.,
Validation of Machine Learning Techniques: Decision Trees and Finite Training Set,
JEI(7), No. 1, January 1998, pp. 94-103. 9807 BibRef

Ho, T.K.[Tin Kam],
The Random Subspace Method for Constructing Decision Forests,
PAMI(20), No. 8, August 1998, pp. 832-844.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9808
Earlier:
C4.5 Decision Forests,
ICPR98(Vol I: 545-549).
WWW Version. 9808 BibRef

Ho, T.K.[Tin Kam],
Exploratory analysis of point proximity in subspaces,
ICPR02(II: 196-199).
WWW Version. 0211 BibRef

Krishnan, R., Sivakumar, G., Bhattacharya, P.,
A Search Technique for Rule Extraction from Trained Neural Networks,
PRL(20), No. 3, March 1999, pp. 273-280. BibRef 9903

Krishnan, R., Sivakumar, G., Bhattacharya, P.,
Extracting Decision Trees from Trained Neural Networks,
PR(32), No. 12, December 1999, pp. 1999-2009.
WWW Version. Decision Tree. BibRef 9912

Suárez, A.[Alberto], Lutsko, J.F.[James F.],
Globally Optimal Fuzzy Decision Trees for Classification and Regression,
PAMI(21), No. 12, December 1999, pp. 1297-1311.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0001 BibRef

Ciampi, A., Diday, E., Lebbe, J., Périnel, E., Vignes, R.,
Growing a tree classifier with imprecise data,
PRL(21), No. 9, August 2000, pp. 787-803. 0008 BibRef

Breiman, L.,
Bagging Predictors,
MachLearn(24), 1996, pp. 123-140. BibRef 9600

Breiman, L.,
Random Forests,
MachLearn(45), No. 1, 2001, pp. 5-32. BibRef 0100

Yang, C.J.[Chang-Jiang], Weng, J.Y.[Ju-Yang],
Visual motion based behavior learning using hierarchical discriminant regression,
PRL(23), No. 8, June 2002, pp. 1031-1038.
HTML Version. 0204 BibRef

Weng, J.Y.[Ju-Yang], Hwang, W.S.[Wey-Shiuan],
Incremental hierarchical discriminant regression for online image classification,
ICDAR01(476-480).
WWW Version. 0109 BibRef

Sethi, I.K., Chatterjee, B.,
Efficient decision tree design for discrete variable pattern recognition problems,
PR(9), No. 4, 1977, pp. 197-206.
WWW Version. 0309 BibRef

Rounds, E.M.,
A combined nonparametric approach to feature selection and binary decision tree design,
PR(12), No. 5, 1980, pp. 313-317.
WWW Version. 0309 BibRef

Murthy, K.R.K., Keerthi, S.S., Murty, M.N.,
Rule prepending and post-pruning approach to incremental learning of decision lists,
PR(34), No. 8, August 2001, pp. 1697-1699.
WWW Version. 0105 BibRef

Priebe, C.E.[Carey E.], Marchette, D.J.[David J.], Healy, Jr., D.M.[Dennis M.],
Integrated Sensing and Processing Decision Trees,
PAMI(26), No. 6, June 2004, pp. 699-708.
IEEE Abstract. IEEE Top Reference. 0404Optimize misclassification rate. BibRef

Haskell, R.E.[Richard E.], Lee, C.[Charles], Hanna, D.M.[Darrin M.],
Geno-fuzzy classification trees,
PR(37), No. 8, August 2004, pp. 1653-1659.
WWW Version. 0407 BibRef

Päivinen, N.[Niina],
Clustering with a minimum spanning tree of scale-free-like structure,
PRL(26), No. 7, 15 May 2005, pp. 921-930.
WWW Version. 0506 BibRef

Pedrycz, W., Sosnowski, Z.A.,
Genetically Optimized Fuzzy Decision Trees,
SMC-B(35), No. 3, June 2005, pp. 633-641.
WWW Version. 0508 BibRef

Pedrycz, W., Sosnowski, Z.A.,
C-fuzzy decision trees,
SMC-C(35), No. 4, November 2005, pp. 498-511.
WWW Version. 0512 BibRef

Rokach, L., Maimon, O.[Oded],
Top-down induction of decision trees classifiers: A survey,
SMC-C(35), No. 4, November 2005, pp. 476-487.
WWW Version. 0512 Survey, Decision Tree. BibRef

Zambon, M.[Michael], Lawrence, R.[Rick], Bunn, A.[Andrew], Powell, S.[Scott],
Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis,
PhEngRS(72), No. 1, January 2006, pp. 25-31.
WWW Version. Alternative splitting rules for classification tree analysis had only minor effects on overall accuracy results of classified imagery, although, individual class accuracies varied widely. 0602 BibRef

van de Vlag, D.E., Stein, A.,
Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees,
GeoRS(45), No. 1, January 2007, pp. 237-245.
WWW Version. 0701 BibRef

Banfield, R.E.[Robert E.], Hall, L.O.[Lawrence O.], Bowyer, K.W.[Kevin W.], Kegelmeyer, W.P.,
A Comparison of Decision Tree Ensemble Creation Techniques,
PAMI(29), No. 1, January 2007, pp. 173-180.
WWW Version. 0701Evaluate Bagging and 7 other randomized based approaches for combinations. Randomized C4.5 ( See also Random Forests. ) Random subspaces ( See also Random Subspace Method for Constructing Decision Forests, The. ) Random Forests ( See also Random Forests. ) AdaBoost M1W ( See also How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code. ) and Bagging BibRef

Pérez, J.M.[Jesús M.], Muguerza, J.[Javier], Arbelaitz, O.[Olatz], Gurrutxaga, I.[Ibai], Martín, J.I.[José I.],
Combining multiple class distribution modified subsamples in a single tree,
PRL(28), No. 4, 1 March 2007, pp. 414-422.
WWW Version. 0701Class distribution; Decision tree; Sampling; Comprehensibility; C4.5 BibRef

Bertrand, G.[Gilles],
On the dynamics,
IVC(25), No. 4, April 2007, pp. 447-454.
WWW Version. 0702Mathematical morphology; Dynamics; Graph; Watershed; Minimum spanning tree; Component tree Necessary and sufficient conditions which indicate when a transformation preserves the dynamics of the regional maxima. BibRef

Li, Y.J.[Yu-Jian],
A clustering algorithm based on maximal theta-distant subtrees,
PR(40), No. 5, May 2007, pp. 1425-1431.
WWW Version. 0702Maximal theta-distant subtree; Minimal spanning tree; Clustering algorithm; Threshold cutting; Number of clusters BibRef

Yildiz, O.T.[Olcay Taner], Dikmen, O.[Onur],
Parallel univariate decision trees,
PRL(28), No. 7, May 2007, pp. 825-832.
WWW Version. 0703Decision trees; Parallel processing; Univariate decision trees; Linear discriminant trees BibRef

Liu, Y.H.[Ying-Ho], Lin, C.C.[Chin-Chin], Lin, W.H.[Wen-Hsiung], Chang, F.[Fu],
Accelerating feature-vector matching using multiple-tree and sub-vector methods,
PR(40), No. 9, September 2007, pp. 2392-2399.
WWW Version. 0705Deterministic approach; Decision trees; Fast matching method; Feature-vector matching; Multiple trees; Statistical approach; Sub-vector matching BibRef

Altincay, H.[Hakan],
Decision trees using model ensemble-based nodes,
PR(40), No. 12, December 2007, pp. 3540-3551.
WWW Version. 0709Decision trees; Ensemble-based decision nodes; Model selection; Omnivariate decision trees; Random subspace method BibRef

Jin, S.[Shuyuan], Yeung, D.S.[Daniel So], Wang, X.Z.[Xi-Zhao],
Network intrusion detection in covariance feature space,
PR(40), No. 8, August 2007, pp. 2185-2197.
WWW Version. 0704Covariance feature space; Threshold based detection; Decision tree; Network intrusion detection; Detection effectiveness BibRef

Balagani, K.S.[Kiran S.], Phoha, V.V.[Vir V.],
On the Relationship Between Dependence Tree Classification Error and Bayes Error Rate,
PAMI(29), No. 10, October 2007, pp. 1866-1868.
WWW Version. 0710Analyze the results of: See also Comments on approximating discrete probability distributions with dependence trees. Derive a better description. BibRef

Pino-Mejias, R.[Rafael], Jimenez-Gamero, M.D.[Maria-Dolores], Cubiles-de-la-Vega, M.D.[Maria-Dolores], Pascual-Acosta, A.[Antonio],
Reduced bootstrap aggregating of learning algorithms,
PRL(29), No. 3, 1 February 2008, pp. 265-271.
WWW Version. 0801Bagging; Reduced bootstrap; Decision trees; Multilayer perceptron BibRef

Watanachaturaporn, P.[Pakorn], Arora, M.K.[Manoj K.], Varshney, P.K.[Pramod K.],
Multisource Classification Using Support Vector Machines: An Empirical Comparison with Decision Tree and Neural Network Classifiers,
PhEngRS(74), No. 2, February 2008, pp. 239-246.
WWW Version. 0803An SVM based multi-source classification shows a significant increase in the classification accuracy with incorporation of ancillary data over the classification performed solely on the basis of spectral data from remote sensing sensors. BibRef

Twala, B.E.T.H., Jones, M.C., Hand, D.J.,
Good methods for coping with missing data in decision trees,
PRL(29), No. 7, 1 May 2008, pp. 950-956.
WWW Version. 0804C4.5; CART; EM algorithm; Fractional cases; Missingness as attribute; Multiple imputation BibRef

Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.],
Learning Policies for Efficiently Identifying Objects of Many Classes,
ICPR06(III: 356-361).
WWW Version. 0609 BibRef

Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.], Greiner, R.[Russell],
Learning to Detect Objects of Many Classes Using Binary Classifiers,
ECCV06(I: 352-364).
WWW Version. 0608Create a decision tree classifier, where each node is based on Viola-Jones ( See also Robust Real-Time Face Detection. ). BibRef

Gangaputra, S.[Sachin], Geman, D.[Donald],
A Design Principle for Coarse-to-Fine Classification,
CVPR06(II: 1877-1884).
WWW Version. 0606Nested representation of binary classifiers. BibRef

Lee, S.[Sengtai], Kim, J.[Jeehoon], Baek, J.Y.[Jae-Yeon], Han, M.W.[Man-Wi], Kim, S.[Sungshin], Chon, T.S.[Tae-Soo],
Pattern Analysis of Movement Behavior of Medaka (Oryzias latipes) A Decision Tree Approach,
CAIP05(546).
WWW Version. 0509 BibRef

Lee, J.S.[Jin-Seon], Oh, I.S.[Il-Seok],
Binary classification trees for multi-class classification problems,
ICDAR03(770-774).
IEEE Abstract. IEEE Top Reference. 0311 BibRef

Windeatt, T., Ardeshir, G.,
Tree pruning for output coded ensembles,
ICPR02(II: 92-95).
WWW Version. 0211Convert a multi-class problem into several binary subproblems with an ensemble of binary classifiers. BibRef

Cho, S.Y.[Siu-Yeung], Chi, Z.[Zheru], Wang, Z.Y.[Zhi-Yong], Siu, W.C.[Wan-Chi],
Robust learning in adaptive processing of data structures for tree representation based image classification,
ICPR02(II: 108-111).
WWW Version. 0211 BibRef

Aguirre, M., Barner, K.,
Multiresolution Permutation Filters Based on Decision Trees,
ICIP00(Vol I: 924-927).
IEEE Abstract. IEEE Top Reference. 0008 BibRef

Salvador-Sanchez, J.[Jose], Pla, F.[Filiberto], Ferri, F.[Francesc],
A Voronoi-Diagram-Based Approach to Oblique Decision Tree Induction,
ICPR98(Vol I: 542-544).
WWW Version. 9808 BibRef

Lange, M., Ganebnykh, S.,
Tree-like data structures for effective recognition of 2-D solids,
ICPR04(I: 592-595).
WWW Version. 0409 BibRef

Lange, M.M.[Michael M.],
Fast Pattern Recognition on a Base of Recursive Representation with Binary Trees,
SCIA97(xx-yy) 9705
HTML Version. BibRef

Imiya, A., Fujiwara, Y.,
Reconstruction, Recognition, and Representation of Trees,
ICPR96(I: 595-600).
WWW Version. 9608(Chiba Univ., D) BibRef

Yoshii, H.,
Pyramid Architecture Classification Tree,
ICPR96(II: 310-314).
WWW Version. 9608(Canon Inc., J) BibRef

Mottl, V.[Vadim], Kostin, A., Muchnik, I., Blinov, A., Kopylov, A.,
Variational Methods in Signal and Image Analysis,
ICPR98(Vol I: 525-527).
WWW Version. 9808 BibRef

Mottl, V., Muchnik, I., Blinov, A., Kopylov, A.,
Hidden Tree-Like Quasi-Markov Model and Generalized Technique for a Class of Image Processing Problems,
ICPR96(II: 715-719).
WWW Version. 9608(Tula State Univ., RUS) BibRef

Gracia, I., Pla, F., Ferri, F.J., García, P.,
Estimating feature discriminant power in decision tree classifiers,
CAIP95(612-617).
WWW Version. 9509 BibRef

Vorácek, J.[Jan],
Tree neural classifier for character recognition,
CAIP95(631-636).
WWW Version. 9509 BibRef

Shepherd, B.A.,
An Appraisal of a Decision Tree Approach to Image Classification,
IJCAI83(473-475). BibRef 8300

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Context and Structure for Classification .