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Classification Trees
, 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 ..."
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Cited by 1 (0 self)
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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
 Statistics and Computing
, 1992
"... Algorithms for learning cIassification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging. T ..."
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Cited by 145 (8 self)
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Algorithms for learning cIassification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging
Split Selection Methods for Classification Trees
 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 ..."
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Cited by 122 (11 self)
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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
"... Abstract. We propose a new algorithm for learning isotonic classification trees. It relabels nonmonotone 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 ..."
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Cited by 4 (1 self)
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Abstract. We propose a new algorithm for learning isotonic classification trees. It relabels nonmonotone 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
"... Abstractâ€”The separation of source coding into two stages, modeling and encoding, is a highly successful approach. We propose metamodeling 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
 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 ..."
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Cited by 12 (4 self)
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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
 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 ..."
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Cited by 4 (1 self)
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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
, 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 ..."
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Cited by 8 (3 self)
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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
ModelBased Classification Trees
 IEEE Transactions on Information Theory
, 1998
"... The construction of classification trees is nearly always topdown, locally optimal and datadriven. 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 ..."
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Cited by 7 (2 self)
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The construction of classification trees is nearly always topdown, locally optimal and datadriven. 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
 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 ..."
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Cited by 73 (11 self)
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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
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342,165