Results 1  10
of
654
A Study of CrossValidation and Bootstrap for Accuracy Estimation and Model Selection
 INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 1995
"... We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), te ..."
Abstract

Cited by 1041 (11 self)
 Add to MetaCart
(Show Context)
We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), tenfold crossvalidation may be better than the more expensive leaveoneout crossvalidation. We report on a largescale experiment  over half a million runs of C4.5 and a NaiveBayes algorithm  to estimate the effects of different parameters on these algorithms on realworld datasets. For crossvalidation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for realword datasets similar to ours, the best method to use for model selection is tenfold stratified cross validation, even if computation power allows using more folds.
Neural Network Ensembles, Cross Validation, and Active Learning
 Advances in Neural Information Processing Systems
, 1995
"... Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members averaged over unlabeled data, so it quant ..."
Abstract

Cited by 445 (6 self)
 Add to MetaCart
(Show Context)
Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members averaged over unlabeled data, so it quantifies the disagreement among the networks. It is discussed how to use the ambiguity in combination with crossvalidation to give a reliable estimate of the ensemble generalization error, and how this type of ensemble crossvalidation can sometimes improve performance. It is shown how to estimate the optimal weights of the ensemble members using unlabeled data. By a generalization of query by committee, it is finally shown how the ambiguity can be used to select new training data to be labeled in an active learning scheme. 1 INTRODUCTION It is well known that a combination of many different predictors can improve predictions. In the neural networks community "ensembles" of neural networks h...
When Networks Disagree: Ensemble Methods for Hybrid Neural Networks
, 1993
"... This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argu ..."
Abstract

Cited by 326 (2 self)
 Add to MetaCart
(Show Context)
This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, we construct a hybrid estimator which is as good or better in the MSE sense than any estimator in the population. We argue that the ensemble method presented has several properties: 1) It efficiently uses all the networks of a population  none of the networks need be discarded. 2) It efficiently uses all the available data for training without overfitting. 3) It inherently performs regularization by smoothing in functional space which helps to avoid overfitting. 4) It utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima. 5) It is ideally suited for parallel computation. 6) It leads to a very useful and natural measure of the number of distinct estimators in a population. 7) The optimal parameters of the ensemble estimator are given in clo...
SemiMarkov conditional random fields for information extraction
 In Advances in Neural Information Processing Systems 17
, 2004
"... We describe semiMarkov conditional random fields (semiCRFs), a conditionally trained version of semiMarkov chains. Intuitively, a semiCRF on an input sequence x outputs a “segmentation ” of x, in which labels are assigned to segments (i.e., subsequences) of x rather than to individual elements x ..."
Abstract

Cited by 236 (10 self)
 Add to MetaCart
(Show Context)
We describe semiMarkov conditional random fields (semiCRFs), a conditionally trained version of semiMarkov chains. Intuitively, a semiCRF on an input sequence x outputs a “segmentation ” of x, in which labels are assigned to segments (i.e., subsequences) of x rather than to individual elements xi of x. Importantly, features for semiCRFs can measure properties of segments, and transitions within a segment can be nonMarkovian. In spite of this additional power, exact learning and inference algorithms for semiCRFs are polynomialtime—often only a small constant factor slower than conventional CRFs. In experiments on five named entity recognition problems, semiCRFs generally outperform conventional CRFs. 1
Popular ensemble methods: an empirical study
 Journal of Artificial Intelligence Research
, 1999
"... An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Baggi ..."
Abstract

Cited by 235 (3 self)
 Add to MetaCart
(Show Context)
An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier – especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble’s performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees. 1.
Bias plus variance decomposition for zeroone loss functions
 In Machine Learning: Proceedings of the Thirteenth International Conference
, 1996
"... We present a biasvariance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The biasvariance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no ..."
Abstract

Cited by 202 (4 self)
 Add to MetaCart
(Show Context)
We present a biasvariance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The biasvariance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was o ered for the more commonly used zeroone (misclassi cation) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition su ers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequencybased estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository. 1
Multilabel classification: An overview
 Int J Data Warehousing and Mining
, 2007
"... Nowadays, multilabel classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multilabel classification, organizes the sparse related literature into a ..."
Abstract

Cited by 190 (9 self)
 Add to MetaCart
Nowadays, multilabel classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multilabel classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multilabel nature of a data set.
MachineLearning Research  Four Current Directions
"... Machine Learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up super ..."
Abstract

Cited by 136 (1 self)
 Add to MetaCart
Machine Learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models.