5,620 research outputs found
Popular Ensemble Methods: An Empirical Study
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 and Shapire, 1996; Shapire, 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
Vote-boosting ensembles
Vote-boosting is a sequential ensemble learning method in which the
individual classifiers are built on different weighted versions of the training
data. To build a new classifier, the weight of each training instance is
determined in terms of the degree of disagreement among the current ensemble
predictions for that instance. For low class-label noise levels, especially
when simple base learners are used, emphasis should be made on instances for
which the disagreement rate is high. When more flexible classifiers are used
and as the noise level increases, the emphasis on these uncertain instances
should be reduced. In fact, at sufficiently high levels of class-label noise,
the focus should be on instances on which the ensemble classifiers agree. The
optimal type of emphasis can be automatically determined using
cross-validation. An extensive empirical analysis using the beta distribution
as emphasis function illustrates that vote-boosting is an effective method to
generate ensembles that are both accurate and robust
On PAC-Bayesian Bounds for Random Forests
Existing guarantees in terms of rigorous upper bounds on the generalization
error for the original random forest algorithm, one of the most frequently used
machine learning methods, are unsatisfying. We discuss and evaluate various
PAC-Bayesian approaches to derive such bounds. The bounds do not require
additional hold-out data, because the out-of-bag samples from the bagging in
the training process can be exploited. A random forest predicts by taking a
majority vote of an ensemble of decision trees. The first approach is to bound
the error of the vote by twice the error of the corresponding Gibbs classifier
(classifying with a single member of the ensemble selected at random). However,
this approach does not take into account the effect of averaging out of errors
of individual classifiers when taking the majority vote. This effect provides a
significant boost in performance when the errors are independent or negatively
correlated, but when the correlations are strong the advantage from taking the
majority vote is small. The second approach based on PAC-Bayesian C-bounds
takes dependencies between ensemble members into account, but it requires
estimating correlations between the errors of the individual classifiers. When
the correlations are high or the estimation is poor, the bounds degrade. In our
experiments, we compute generalization bounds for random forests on various
benchmark data sets. Because the individual decision trees already perform
well, their predictions are highly correlated and the C-bounds do not lead to
satisfactory results. For the same reason, the bounds based on the analysis of
Gibbs classifiers are typically superior and often reasonably tight. Bounds
based on a validation set coming at the cost of a smaller training set gave
better performance guarantees, but worse performance in most experiments
Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance
Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, âAP+DPâ and âDP+APâ as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. âAP+DPâ method generally performs better than the âDP+APâ method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method âAP+DPâ improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that âAP+DPâ outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy
Bagging ensemble selection
Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and a number of data mining competitions on the Kaggle platform. In this paper we present a novel variant: bagging ensemble selection. Three variations of the proposed algorithm are compared to the original ensemble selection algorithm and other ensemble algorithms. Experiments with ten real world problems from diverse domains demonstrate the benefit of the bagging ensemble selection algorithm
Ensembles of probability estimation trees for customer churn prediction
Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both
An empirical evaluation of imbalanced data strategies from a practitioner's point of view
This research tested the following well known strategies to deal with binary
imbalanced data on 82 different real life data sets (sampled to imbalance rates
of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline
(just the base classifier). As base classifiers we used SVM with RBF kernel,
random forests, and gradient boosting machines and we measured the quality of
the resulting classifier using 6 different metrics (Area under the curve,
Accuracy, F-measure, G-mean, Matthew's correlation coefficient and Balanced
accuracy). The best strategy strongly depends on the metric used to measure the
quality of the classifier. For AUC and accuracy class weight and the baseline
perform better; for F-measure and MCC, SMOTE performs better; and for G-mean
and balanced accuracy, underbagging
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