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A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirtythree Old and New Classification Algorithms
, 2000
"... . Twentytwo decision tree, nine statistical, and two neural network algorithms are compared on thirtytwo datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both cr ..."
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Cited by 167 (7 self)
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. Twentytwo decision tree, nine statistical, and two neural network algorithms are compared on thirtytwo datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, splinebased, algorithm called Polyclass at the top, although it is not statistically signicantly dierent from twenty other algorithms. Another statistical algorithm, logistic regression, is second with respect to the two accuracy criteria. The most accurate decision tree algorithm is Quest with linear splits, which ranks fourth and fth, respectively. Although splinebased statistical algorithms tend to have good accuracy, they also require relatively long training times. Polyclass, for example, is third last in terms of median training time. It often requires hours of training compared to seconds for other algorithms. The Quest and logistic regression algor...
RainForest  a Framework for Fast Decision Tree Construction of Large Datasets
 In VLDB
, 1998
"... Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no algorithm uniformly outperforms all other algorithms in terms of quality. In this paper, we present a unifying framework fo ..."
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Cited by 95 (9 self)
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Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no algorithm uniformly outperforms all other algorithms in terms of quality. In this paper, we present a unifying framework for decision tree classifiers that separates the scalability aspects of algorithms for constructing a decision tree from the central features that determine the quality of the tree. This generic algorithm is easy to instantiate with specific algorithms from the literature (including C4.5, CART,
Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems
 IEEE Transactions on Neural Networks
, 1997
"... In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole ..."
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Cited by 66 (2 self)
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In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state space search, we first describe the general issues in constructive algorithms, with special emphasis on the search strategy. A taxonomy, based on the differences in the state transition mapping, the training algorithm and the network architecture, is then presented. Keywords Constructive algorithm, structure learning, state space search, dynamic node creation, projection pursuit regression, cascadecorrelation, resourceallocating network, group method of data handling. I. Introduction A. Problems with Fixed Size Networks I N recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. Among...
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 42 (8 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 is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in number of missing values. This is an advantage because inferences from the tree structures can be adversely affected by selection bias. The new methods are shown to be highly competitive in terms of computational speed and classification accuracy of future observations. Key words and phrases: Decision tree, linear discriminant analysis, missing value, selection bias. 1
Spatial Contextual Classification and Prediction Models for Mining Geospatial Data
 IEEE Transactions on Multimedia
, 2002
"... Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov Random Fields (MRFs) is a popular model for incorporating spatial context into image segmentation and landuse classification problems. The spatial autoregression ..."
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Cited by 39 (12 self)
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Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov Random Fields (MRFs) is a popular model for incorporating spatial context into image segmentation and landuse classification problems. The spatial autoregression model (SAR) which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas (e.g., solution procedures). We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogous to the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework.
Linear Optimality Theory as a Model of Gradience in Grammar
 In Gradience in Grammar: Generative Perspectives, ed. Gisbert Fanselow, Caroline Féry, Ralph Vogel, and Matthias Schlesewsky
, 2005
"... This paper provides an overview of Linear Optimality Theory (LOT), a variant of Optimality Theory (OT) designed for the modeling of gradient acceptability judgment data. We summarize the empirical properties of gradient data that have been reported in the experimental literature, and use them to mot ..."
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Cited by 26 (0 self)
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This paper provides an overview of Linear Optimality Theory (LOT), a variant of Optimality Theory (OT) designed for the modeling of gradient acceptability judgment data. We summarize the empirical properties of gradient data that have been reported in the experimental literature, and use them to motivate the design of LOT. We discuss LOT’s notions of constraint competition and optimality, as well as a new formulation of ranking argumentation, which makes it possible to apply standard parameter estimation techniques to LOT. Then the LOT model is compared to Standard OT, to Harmonic Grammar, and to recently proposed probabilisitic versions of OT. 1.
Constructive Feedforward Neural Networks for Regression Problems: A Survey
, 1995
"... In this paper, we review the procedures for constructing feedforward neural networks in regression problems. While standard backpropagation performs gradient descent only in the weight space of a network with fixed topology, constructive procedures start with a small network and then grow additiona ..."
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Cited by 21 (0 self)
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In this paper, we review the procedures for constructing feedforward neural networks in regression problems. While standard backpropagation performs gradient descent only in the weight space of a network with fixed topology, constructive procedures start with a small network and then grow additional hidden units and weights until a satisfactory solution is found. The constructive procedures are categorized according to the resultant network architecture and the learning algorithm for the network weights. The Hong Kong University of Science & Technology Technical Report Series Department of Computer Science 1 Introduction In recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. Among them, the class of multilayer feedforward networks is perhaps the most popular. Standard backpropagation performs gradient descent only in the weight space of a network with fixed topology; this approach is analogous to ...