On Meta-learning for Dynamic Ensemble Selection

Cruz, Rafael M. O.; Sabourin, Robert; Cavalcanti, George D. C.

doi:10.1109/icpr.2014.221

Cited by 25 publications

(40 citation statements)

References 32 publications

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“…However, in this paper, we compare the results provided in the extensive analysis made in [25] 4. META-DES (MDES): The META-DES method is a dynamic selection ensemble method based on the assumption that the dynamic ensemble selection problem can be considered as a metaproblem [21]. This meta-problem uses different criteria regarding the behavior of a base classifier c i , in order to decide whether it is competent enough to classify a given test sample.…”

Section: Comparative Analysis: Ensemble Generation Methodsmentioning

confidence: 99%

Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

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Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixture (GM) functions as a new approach for providing an efficient combination procedure for these systems through the use of dynamic weights in the combination process. Therefore, we present three GM functions to be applied as a combination method. The main advantage of these functions is that they can define dynamic weights at the member outputs, making the combination process more efficient. In order to evaluate the feasibility of the proposed approach, an empirical analysis is conducted, applying classifier ensembles to 25 different classification data sets. In this analysis, we compare the use of the proposed approaches to ensembles using traditional combination methods as well as the state-of-the-art ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed approaches to the traditional ones as well as comparable results with the state-of-the-art methods. This paper is divided into eight sections and it is organized as follows. In Section 2, we describe some recent studies in classifier ensembles. The fundamental notions of the generalized mixture functions are introduced in Section 3, while the basic concepts of classifier ensembles are presented in Section 4. The proposed approach is presented in Section 5. In Section 6, the experimental methodology is presented, while Section 7 presents an analysis of the obtained results of this work. Finally, Section 7 concludes this paper. Recent Studies in Weighted Combination Methods for in Classifier EnsemblesAs already mentioned, different ways of calculating the weights of each class for each individual classifier can be used in a classifier ensemble [10,39,45,46,50,51,57,70]. Although weighted combination methods appear to provide some flexibility, obtaining the optimal weights is not an easy task. Therefore, some optimization techniques have been applied to define the best set of weights, such as in [4,45,51,57]. In [57], for instance, a genetic algorithm (GA) was used to define an optimized set of weights that are used along with the output of the individual classifiers to define the final output of the classifier ensembles. However, all the aforementioned studies apply procedures to define static weights. In other words, these methods define a set of weights that are used throughout the testing phase. This static way to define weights can eventually become inefficient for a classifier ensemble, since the accuracy of an individual classifier can change in the testing search space and this change is not capture by static weights.In a dynamic weighting process, the outputs of all individual classifiers are aggregated and the most competent ones receive the highest weight values. The competence of the classifier o...

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Section: Comparative Analysis: Ensemble Generation Methodsmentioning

confidence: 99%

Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

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“…In the dynamic selection stage, DFP chooses the classifiers that were preprocessed by the pruning stage.  Margin Distance Minimization (MDM) pruning: The margin distance [37] defines the quantity c between outputs of ensemble classifiers and labels. The Quantity c equals to 1 if the label is correctly classified.…”

Section: Comparison To Other Pruning Methodsmentioning

confidence: 99%

The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction

Huang

Chang

et al. 2019

Appl Intell

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Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input datasets; this difficulty is called the uncertainty prediction problems. In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It achieves the high-quality uncertainty estimation with a small value of the prediction interval width (MPIW) and a high confidence of prediction interval coverage probability (PICP) by using deep ensemble networks. In addition to these networks, unique loss functions are proposed, and these functions make the sub-learners available for standard gradient descent learning. Furthermore, the margin criterion fine-tuning-based Pareto pruning method is introduced to optimize the ensembles. Several experiments including predicting uncertainties of classification and regression are conducted to analyze the performance of MBPEP. The experimental results show that MBPEP achieves a small interval width and a low learning error with an optimal number of ensembles. For the real-world problems, MBPEP performs well on input datasets with unknown distributions datasets incomings and improves learning performance on a multi task problem when compared to that of each single model.

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“…For each cluster, the most accurate classifiers are picked. To subsequently create the ensemble, the most accurate classifiers are selected from the diverse classifiers . In this method, first, the k‐means algorithm divides validation patterns into k groups.…”

Section: Background Materialsmentioning

confidence: 99%

Ensemble cluster pruning via convex‐concave programming

Özöğür‐Akyüz

Otar

Ataş

2020

Computational Intelligence

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Summary Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade‐off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions.

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On Meta-learning for Dynamic Ensemble Selection

Cited by 25 publications

References 32 publications

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Combining multiple algorithms in classifier ensembles using generalized mixture functions

The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction

Ensemble cluster pruning via convex‐concave programming

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