14.4.3 Decision Trees

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Tamura, S.[Shinichi],
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Kurzynski, M.W.[Marek W.],
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Quinlan, J.R.,
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Li, X.B.[Xiao-Bo], Dubes, R.C.[Richard C.],
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Shlien, S.[Seymour],
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Park, Y.T.[Young-Tae], Sklansky, J.[Jack],
Automated design of linear tree classifiers,
PR(23), No. 12, 1990, pp. 1393-1412.
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And:
Fast tree classifiers,
ICPR90(I: 684-686).
IEEE DOI Link 9006
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Earlier:
Automated design of piecewise-linear classifiers of multiple-class data,
ICPR88(II: 1068-1071).
IEEE DOI Link 8811
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Brown, D.E.[Donald E.], Corruble, V.[Vincent], Pittard, C.L.[Clarence Louis],
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Brown, D.E., Pittard, C.L., Park, H.,
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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.
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Guo, H., Gelfand, S.B.,
Classification trees with neural network feature extraction,
CVPR92(183-188).
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Safavian, S.R.[S. Rasoul], and Landgrebe, D.A.[David A.],
A Survey of Decision Tree Classifier Methodology,
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Zhou, X.J.[Xiao Jia], and Dillon, T.S.[Tharam S.],
A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction,
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Draper, B.A., Brodley, C.E., Utgoff, P.E.,
Goal-Directed Classification Using Linear Machine Decision Trees,
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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.
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Lovell, B.C., Bradley, A.P.,
The Multiscale Classifier,
PAMI(18), No. 2, February 1996, pp. 124-137.
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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,
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Kay, J.,
A Comparative-Analysis of Methods for Pruning Decision Trees: Comment,
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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.
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Model 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,
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Friedl, M.A., Brodley, C.E.,
Decision Tree Classification of Land-Cover from Remotely-Sensed Data,
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Jun, B.H., Kim, C.S., Song, H.Y., Kim, J.,
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Chowdhury, N., Murthy, C.A.,
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Lam, C.P., West, G.A.W., Caelli, T.M.,
Validation of Machine Learning Techniques: Decision Trees and Finite Training Set,
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Ho, T.K.[Tin Kam],
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Earlier:
C4.5 Decision Forests,
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Ho, T.K.[Tin Kam],
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ICPR02(II: 196-199).
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Krishnan, R., Sivakumar, G., Bhattacharya, P.,
A Search Technique for Rule Extraction from Trained Neural Networks,
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Krishnan, R., Sivakumar, G., Bhattacharya, P.,
Extracting Decision Trees from Trained Neural Networks,
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Suárez, A.[Alberto], Lutsko, J.F.[James F.],
Globally Optimal Fuzzy Decision Trees for Classification and Regression,
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Ciampi, A., Diday, E., Lebbe, J., Périnel, E., Vignes, R.,
Growing a tree classifier with imprecise data,
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Breiman, L.,
Bagging Predictors,
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Breiman, L.,
Random Forests,
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Sethi, I.K., Chatterjee, B.,
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Rounds, E.M.,
A combined nonparametric approach to feature selection and binary decision tree design,
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Murthy, K.R.K., Keerthi, S.S., Murty, M.N.,
Rule prepending and post-pruning approach to incremental learning of decision lists,
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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.
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Haskell, R.E.[Richard E.], Lee, C.[Charles], Hanna, D.M.[Darrin M.],
Geno-fuzzy classification trees,
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Päivinen, N.[Niina],
Clustering with a minimum spanning tree of scale-free-like structure,
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Pedrycz, W., Sosnowski, Z.A.,
Genetically Optimized Fuzzy Decision Trees,
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Pedrycz, W., Sosnowski, Z.A.,
C-fuzzy decision trees,
SMC-C(35), No. 4, November 2005, pp. 498-511.
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Rokach, L., Maimon, O.[Oded],
Top-down induction of decision trees classifiers: A survey,
SMC-C(35), No. 4, November 2005, pp. 476-487.
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Kennard, M.J., Harch, B.D., Pusey, B.J., and Arthington, A.H.,
Accurately defining the reference condition for summary biotic metrics,
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Zambon, M.[Michael], Lawrence, R.[Rick], Bunn, A.[Andrew], Powell, S.[Scott],
Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis,
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van de Vlag, D.E., Stein, A.,
Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees,
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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.
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Evaluate 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,
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Class distribution; Decision tree; Sampling; Comprehensibility; C4.5 BibRef

Bertrand, G.[Gilles],
On the dynamics,
IVC(25), No. 4, April 2007, pp. 447-454.
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Mathematical 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. 0702
Maximal 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.
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Decision 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,
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WWW Version. 0705
Deterministic 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.
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Decision 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.
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Covariance 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.
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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,
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Bagging; Reduced bootstrap; Decision trees; Multilayer perceptron BibRef

Watanachaturaporn, P.[Pakorn], Arora, M.K.[Manoj K.], Varshney, P.K.[Pramod K.],
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PhEngRS(74), No. 2, February 2008, pp. 239-246.
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An 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,
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C4.5; CART; EM algorithm; Fractional cases; Missingness as attribute; Multiple imputation BibRef

Kang, D.K.[Dae-Ki], Sohn, K.[Kiwook],
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Taxonomy; Decision tree; Propositionalization; Jensen-Shannon divergence measure BibRef

Nichol, J.[Janet], Wong, M.S.[Man Sing],
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Ouyang, J.[Jie], Patel, N.[Nilesh], Sethi, I.[Ishwar],
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Distributed data mining; Decision tree; Fisher linear discriminant BibRef

Liu, J.[Jing], Li, X.[Xue], Zhong, W.[Weicai],
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Data streams; Data mining; Concept drift; Ambiguous decision trees; Incremental learning BibRef

de Sá, J.P.M.[J. P. Marques], Gama, J.[João], Sebastião, R.[Raquel], Alexandre, L.A.[Luís A.],
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Martinez-Munoz, G.[Gonzalo], Larios, N.[Natalia], Mortensen, E.[Eric], Zhang, W.[Wei], Yamamuro, A.[Asako], Paasch, R.[Robert], Payet, N.[Nadia], Lytle, D.[David], Shapiro, L.G.[Linda G.], Todorovic, S.[Sinisa], Moldenke, A.[Andrew], Dietterich, T.G.[Thomas G.],
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Basak, J.[Jayanta],
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Zhong, M.Y.[Ming-Yu], Georgiopoulos, M.[Michael], Anagnostopoulos, G.C.[Georgios C.],
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ICPR08(1-4).
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Sharp, T.[Toby],
Implementing Decision Trees and Forests on a GPU,
ECCV08(IV: 595-608).
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Haynes, K., Liu, X.W.[Xiu-Wen], Mio, W.,
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ICIP06(2753-2756). 0610

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Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.],
Learning Policies for Efficiently Identifying Objects of Many Classes,
ICPR06(III: 356-361).
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Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.], Greiner, R.[Russell],
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ECCV06(I: 352-364).
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Create 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],
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Lee, S.T.[Seng-Tai], Kim, J.[Jeehoon], Baek, J.Y.[Jae-Yeon], Han, M.W.[Man-Wi], Kim, S.[Sungshin], Chon, T.S.[Tae-Soo],
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Lee, J.S.[Jin-Seon], Oh, I.S.[Il-Seok],
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Windeatt, T., Ardeshir, G.,
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Cho, S.Y.[Siu-Yeung], Chi, Z.[Zheru], Wang, Z.Y.[Zhi-Yong], Siu, W.C.[Wan-Chi],
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Aguirre, M., Barner, K.,
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ICIP00(Vol I: 924-927).
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Salvador-Sanchez, J.[Jose], Pla, F.[Filiberto], Ferri, F.J.[Francesc J.],
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ICPR98(Vol I: 542-544).
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Gracia, I., Pla, F., Ferri, F.J., García, P.,
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Lange, M., Ganebnykh, S.,
Tree-like data structures for effective recognition of 2-D solids,
ICPR04(I: 592-595).
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Lange, M.M.[Michael M.],
Fast Pattern Recognition on a Base of Recursive Representation with Binary Trees,
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Imiya, A., Fujiwara, Y.,
Reconstruction, Recognition, and Representation of Trees,
ICPR96(I: 595-600).
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Pyramid Architecture Classification Tree,
ICPR96(II: 310-314).
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Mottl, V.[Vadim], Kostin, A., Muchnik, I., Blinov, A., Kopylov, A.,
Variational Methods in Signal and Image Analysis,
ICPR98(Vol I: 525-527).
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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).
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Vorácek, J.[Jan],
Tree neural classifier for character recognition,
CAIP95(631-636).
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Shepherd, B.A.,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Context and Structure for Classification .