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ACCURATE DECISION TREE CONSTRUCTION

by C. Sudarsana Reddy, J. Naga Muneiah, S. Aquter Babu
"... Abstract — Classification is one of the most important techniques in data mining. Decision tree is the most important classification technique in machine learning and data mining. Decision tree classifiers are constructed using training dada sets. Training data sets contain numerical (or continuous) ..."
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in the training data sets are properly handled (or controlled or modeled or corrected) appropriately. The present study proposes a new algorithm for decision tree classifier construction. This new algorithm is named as Accurate Decision Tree (ADT) classifier construction. ADT classifiers are more accurate than

Inducing Small and Accurate Decision Trees

by Bernhard Pfahringer
"... Recently, the quality improvement of decision trees and classifiers in general achievable by extended search efforts has received quite some attention in the literature. Contrary to the construction of ensembles of classifiers, which aims at improving overall predictive accuracy, our approach aims a ..."
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at improving the intelligibility of a single classifier. Our goal is the induction of a single, small, yet accurate decision tree. We describe a simple prepruning method (PreC4) that uses cross-validation to determine an appropriate stopping point for tree construction in a reliable manner. In addition

Accurate decision trees for mining high-speed data streams

by João Gama - In Proc. SIGKDD , 2003
"... In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT. In this paper we extend th ..."
Abstract - Cited by 53 (6 self) - Add to MetaCart
In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT. In this paper we extend

Induction of Decision Trees

by J. R. Quinlan - MACH. LEARN , 1986
"... The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such syste ..."
Abstract - Cited by 4303 (4 self) - Add to MetaCart
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one

Accurate Unlexicalized Parsing

by Dan Klein, Christopher D. Manning - IN PROCEEDINGS OF THE 41ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS , 2003
"... We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its ..."
Abstract - Cited by 1026 (70 self) - Add to MetaCart
We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its

An experimental comparison of three methods for constructing ensembles of decision trees

by Thomas G. Dietterich, Doug Fisher - Bagging, boosting, and randomization. Machine Learning , 2000
"... Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approac ..."
Abstract - Cited by 604 (6 self) - Add to MetaCart
of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than

Probabilistic Part-of-Speech Tagging Using Decision Trees

by Helmut Schmid , 1994
"... In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method, ..."
Abstract - Cited by 1009 (9 self) - Add to MetaCart
In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method

Very simple classification rules perform well on most commonly used datasets

by Robert C. Holte - Machine Learning , 1993
"... The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest to the machin ..."
Abstract - Cited by 542 (5 self) - Add to MetaCart
;quot; pruning method in Mingers (1989). This method produced the most accurate decision trees, and in four of the five domains studied these trees had only 2 or 3 leaves

Games and decisions

by Debra Edwards Ph. D , 1957
"... Agency ..."
Abstract - Cited by 610 (0 self) - Add to MetaCart
Abstract not found

Decision-Theoretic Planning: Structural Assumptions and Computational Leverage

by Craig Boutilier, Thomas Dean, Steve Hanks - JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH , 1999
"... Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives ..."
Abstract - Cited by 510 (4 self) - Add to MetaCart
Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions
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