Results 1  10
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108
Tree Induction for Probabilitybased Ranking
, 2002
"... Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., c ..."
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Cited by 161 (4 self)
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Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probabilitybased rankings, and by how much. In this paper we first discuss why the decisiontree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decisiontree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reducederror pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straghtforward methods for improving probabilitybased rankings. We show that using a simple, common smoothing methodthe Laplace correctionuniformly improves probabilitybased rankings. In addition, bagging substantioJly improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on classmembership probability are required.
Efficient Progressive Sampling
, 1999
"... Having access to massiveamounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size is rarely obvious. We analyze methods for progressive samplingstarting with ..."
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Cited by 116 (10 self)
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Having access to massiveamounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size is rarely obvious. We analyze methods for progressive samplingstarting with small samples and progressively increasing them as long as model accuracy improves. We show that a simple, geometric sampling schedule is efficient in an asymptotic sense. We then explore the notion of optimal efficiency: what is the absolute best sampling schedule? We describe the issues involved in instantiating an "optimally efficient" progressive sampler. Finally,we provide empirical results comparing a variety of progressive sampling methods. We conclude that progressive sampling often is preferable to analyzing all data instances.
Data Mining in Soft Computing Framework: A Survey
 IEEE Transactions on Neural Networks
, 2001
"... The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the mode ..."
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Cited by 109 (3 self)
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The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in datarich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.
Tree induction vs. logistic regression: A learningcurve analysis
 CEDER WORKING PAPER #IS0102, STERN SCHOOL OF BUSINESS
, 2001
"... Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership pr ..."
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Cited by 86 (16 self)
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Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership probabilities. We use a learningcurve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (1) Contrary to prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning curves cross), so conclusions about inductionalgorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective atproducing probabilitybased rankings, although apparently comparatively less so foragiven training{set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable canbecharacterized surprisingly well by a simple measure of signaltonoise ratio.
Simpoint 3.0: Faster and more flexible program analysis
 Journal of Instruction Level Parallelism
, 2005
"... This paper describes the new features available in the SimPoint 3.0 release. The release provides two techniques for drastically reducing the runtime of SimPoint: faster searching to find the best clustering, and efficiently clustering large numbers of intervals. SimPoint 3.0 also provides an opti ..."
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Cited by 77 (4 self)
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This paper describes the new features available in the SimPoint 3.0 release. The release provides two techniques for drastically reducing the runtime of SimPoint: faster searching to find the best clustering, and efficiently clustering large numbers of intervals. SimPoint 3.0 also provides an option to output only the simulation points that represent the majority of execution, which can reduce simulation time without much increase in error. Finally, this release provides support for correctly clustering variable length intervals, taking into consideration the weight of each interval during clustering. This paper describes SimPoint 3.0’s new features, how to use them, and points out some common pitfalls. 1
Understanding the crucial role of attribute interaction in data mining.
 Artificial Intelligence Review
, 2001
"... Abstract. This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different ..."
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Cited by 62 (18 self)
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Abstract. This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different kinds of problem in data mining, such as attribute construction, coping with small disjuncts, induction of firstorder logic rules, detection of Simpson's paradox, and finding several types of interesting rules. Hence, a better understanding of attribute interaction can lead to a better understanding of the relationship between these kinds of problems, which are usually studied separately from each other. Secondly, we draw attention to the fact that most rule induction algorithms are based on a greedy search which does not cope well with the problem of attribute interaction, and point out some alternative kinds of rule discovery methods which tend to cope better with this problem. Thirdly, we discussed several algorithms and methods for discovering interesting knowledge that, implicitly or explicitly, are based on the concept of attribute interaction.
Toward Intelligent Assistance for a Data Mining Process: An OntologyBased Approach for CostSensitive Classification
 IEEE Transactions on Knowledge and Data Engineering
, 2005
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Feature Subset Selection by Bayesian networks: a comparison with genetic and sequential algorithms
"... In this paper we perform a comparison among FSSEBNA, a randomized, populationbased and evolutionary algorithm, and two genetic and other two sequential search approaches in the well known Feature Subset Selection (FSS) problem. In FSSEBNA, the FSS problem, stated as a search problem, uses the E ..."
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Cited by 54 (13 self)
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In this paper we perform a comparison among FSSEBNA, a randomized, populationbased and evolutionary algorithm, and two genetic and other two sequential search approaches in the well known Feature Subset Selection (FSS) problem. In FSSEBNA, the FSS problem, stated as a search problem, uses the EBNA (Estimation of Bayesian Network Algorithm) search engine, an algorithm within the EDA (Estimation of Distribution Algorithm) approach. The EDA paradigm is born from the roots of the GA community in order to explicitly discover the relationships among the features of the problem and not disrupt them by genetic recombination operators. The EDA paradigm avoids the use of recombination operators and it guarantees the evolution of the population of solutions and the discovery of these relationships by the factorization of the probability distribution of best individuals in each generation of the search. In EBNA, this factorization is carried out by a Bayesian network induced by a chea...
WellTrained PETs: Improving Probability Estimation Trees
, 2000
"... Decision trees are one of the most effective and widely used classification methods. However, many applications require class probability estimates, and probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in ..."
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Cited by 53 (6 self)
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Decision trees are one of the most effective and widely used classification methods. However, many applications require class probability estimates, and probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probability estimates, and by how much. In this paper we first discuss why the decisiontree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decisiontree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reducederror pruning). Larger tree...
Learning ensembles from bites: A scalable and accurate approach
"... Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small vo ..."
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Cited by 48 (7 self)
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Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small votes") is a promising approach for learning from massive datasets, one that can utilize the power of boosting and bagging. We propose a framework for building hundreds or thousands of such classifiers on small subsets of data in a distributed environment. Experiments show this approach is fast, accurate, and scalable.