Comparing pure parallel ensemble creation techniques against bagging

Hall, Lawrence O.; Bowyer, Kevin W.; Banfield, Robert E.; Bhadoria, Divya; Kegelmeyer, W. Philip; Eschrich, Steven A.

doi:10.1109/icdm.2003.1250970

Cited by 16 publications

(12 citation statements)

References 16 publications

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“…Bagging is more accurate than variations of random forests, randomized C4.5 [13] and random subspaces [14]. For data sets with many conflicting cases bagging is much better than boosting [15].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm

Cohagan

Grzymała-Busse

Hippe

2010

Intelligent Data Engineering and Automated Learning – IDEAL 2010

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Abstract. This paper presents results of experiments on some data sets using bagging on the MLEM2 rule induction algorithm. Three different methods of ensemble voting, based on support (a non-democratic voting in which ensembles vote with their strengths), strength only (an ensemble with the largest strength decides to which concept a case belongs) and democratic voting (each ensemble has at most one vote) were used. Our conclusions are that though in most cases democratic voting was the best, it is not significantly better than voting based on support. The strength voting was the worst voting method.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm

Cohagan

Grzymała-Busse

Hippe

2010

Intelligent Data Engineering and Automated Learning – IDEAL 2010

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“…Representative parallel ensemble approaches include Bagging [4] and Random Forests [5]; and examples of sequential ensemble approaches include AdaBoost [8] and Stochastic Gradient Boosting [9]. However, despite the emergence of many ensemble approaches, it has been shown that none is significantly superior to another over a range of data sets [10].…”

Section: Introductionmentioning

confidence: 99%

Cocktail Ensemble for Regression

Zhou²,

Ting³

2007

Seventh IEEE International Conference on Data Mining (ICDM 2007)

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“…Some examples for ensemble techniques are bagging, boosting, and random forests. 31 Usually ensemble techniques are not used for strong classifiers like support vector machines. However, the question arises whether ensemble techniques are able to improve results of such classifiers even more.…”

Section: New Kernels For Support Vector Machinesmentioning

confidence: 99%

“…[1][2][3]18 It has been discovered that a combination of different classifications into a consensus model can improve the overall results of the classification. The basic algorithms behind were decision treemethodslikerecursivepartitioning,ensemblemethods 22,23,[29][30][31][32] like adaptive boosting, 33 and support vector machines. 34 However, the limiting but most important part is the access to validated data.…”

Section: Introductionmentioning

confidence: 99%

Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques.

Eitrich¹,

Kless²,

Druska³

et al. 2007

ChemInform

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In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.

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Comparing pure parallel ensemble creation techniques against bagging

Cited by 16 publications

References 16 publications

A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm

A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm

Cocktail Ensemble for Regression

Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques.

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