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Classification Trees

by D.G.T. Denison, B. K. Mallick , 1998
"... The generalized linear model framework is often used in classification problems and the importance and effect of the predictor variables on the response is generally judged by examination of the relevant regression coefficients. This chapter describes classification trees which can also be used for ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
The generalized linear model framework is often used in classification problems and the importance and effect of the predictor variables on the response is generally judged by examination of the relevant regression coefficients. This chapter describes classification trees which can also be used

Learning classification trees

by Wray Buntine, Wray Buntine - Statistics and Computing , 1992
"... Algorithms for learning cIassification trees have had successes in ar-tificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statis-tics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging. T ..."
Abstract - Cited by 145 (8 self) - Add to MetaCart
Algorithms for learning cIassification trees have had successes in ar-tificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statis-tics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging

Split Selection Methods for Classification Trees

by Wei-Yin Loh, Yu-Shan Shih - STATISTICA SINICA , 1997
"... Classification trees based on exhaustive search algorithms tend to be biased towards selecting variables that afford more splits. As a result, such trees should be interpreted with caution. This article presents an algorithm called QUEST that has negligible bias. Its split selection strategy shares ..."
Abstract - Cited by 122 (11 self) - Add to MetaCart
Classification trees based on exhaustive search algorithms tend to be biased towards selecting variables that afford more splits. As a result, such trees should be interpreted with caution. This article presents an algorithm called QUEST that has negligible bias. Its split selection strategy shares

Isotonic Classification Trees

by Rémon Van De Kamp, Ad Feelders, Nicola Barile
"... Abstract. We propose a new algorithm for learning isotonic classification trees. It relabels non-monotone leaf nodes by performing the isotonic regression on the collection of leaf nodes. In case two leaf nodes with a common parent have the same class after relabeling, the tree is pruned in the pare ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
Abstract. We propose a new algorithm for learning isotonic classification trees. It relabels non-monotone leaf nodes by performing the isotonic regression on the collection of leaf nodes. In case two leaf nodes with a common parent have the same class after relabeling, the tree is pruned

Classification Tree Sources

by Edgar Binder, Manfred Kufleitner
"... Abstract—The separation of source coding into two stages, modeling and encoding, is a highly successful approach. We propose meta-modeling as an additional stage. As an application, we use this paradigm to deduce an efficient and optimal algorithm for a novel and powerful model set: the classificati ..."
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: the classification tree sources. Our results on classification tree sources unify and generalize prior results for tree sources. Moreover, we point out applications in text and image compression. I.

Pruning for monotone classification trees

by Ad Feelders, Martijn Pardoel - Lecture Notes in Computer Science
"... Abstract. For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explanatory attributes. These are called classification problems with monotonicity constraints. Standard classification tree algorithms such as CART or C4.5 are not g ..."
Abstract - Cited by 12 (4 self) - Add to MetaCart
Abstract. For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explanatory attributes. These are called classification problems with monotonicity constraints. Standard classification tree algorithms such as CART or C4

A Classification Tree for Speciation

by Alain Petrowski, Marc Girod, Marc Girod Genet, Departement Informatique, Departement Informatique - In Proceedings of CEC 1999 , 1999
"... The most efficient speciation methods suffer from a quite high complexity from O(n c(n)) to O(n 2 ), where c(n) is a factor that can be proportional to n, the population size. In this paper, a speciation method based on a classification tree is presented, having a complexity of O(n log n). The pop ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
The most efficient speciation methods suffer from a quite high complexity from O(n c(n)) to O(n 2 ), where c(n) is a factor that can be proportional to n, the population size. In this paper, a speciation method based on a classification tree is presented, having a complexity of O(n log n

IMPROVING THE PRECISION OF CLASSIFICATION TREES

by Wei-yin Loh , 2009
"... Besides serving as prediction models, classification trees are useful for finding important predictor variables and identifying interesting subgroups in the data. These functions can be compromised by weak split selection algorithms that have variable selection biases or that fail to search beyond l ..."
Abstract - Cited by 8 (3 self) - Add to MetaCart
Besides serving as prediction models, classification trees are useful for finding important predictor variables and identifying interesting subgroups in the data. These functions can be compromised by weak split selection algorithms that have variable selection biases or that fail to search beyond

Model-Based Classification Trees

by Donald Geman, Bruno Jedynak - IEEE Transactions on Information Theory , 1998
"... The construction of classification trees is nearly always top-down, locally optimal and data-driven. Such recursive designs are often globally inefficient, for instance in terms of the mean depth necessary to reach a given classification rate. We consider statistical models for which exact global op ..."
Abstract - Cited by 7 (2 self) - Add to MetaCart
The construction of classification trees is nearly always top-down, locally optimal and data-driven. Such recursive designs are often globally inefficient, for instance in terms of the mean depth necessary to reach a given classification rate. We consider statistical models for which exact global

Classification trees with unbiased multiway splits

by Hyunjoong Kim, Wei-yin Loh - Journal of the American Statistical Association , 2001
"... Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods i ..."
Abstract - Cited by 73 (11 self) - Add to MetaCart
Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods
Results 1 - 10 of 342,165