Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from Multi-objective Evolutionary Algorithms

Fernández, Juan Carlos Fernández; Cruz-Ramírez, M.; Hervás-Martı́nez, César

doi:10.1007/s00521-016-2781-y

Cited by 10 publications

(13 citation statements)

References 56 publications

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“…6. P2-SA and P2-WTA: These are two versions of a multi-objective algorithm for selecting members for an ensemble system, proposed in [34]. Table 4 presents the accuracy levels of all ten ensemble methods.…”

Section: Comparative Analysis: Ensemble Generation Methodsmentioning

confidence: 99%

“…The results of the first two methods (Random Forest and XGBoost) were obtained by an implementation done by us. However, for the remaining five methods, we used the original results, provided in [25] and [34]. In these papers, a different experimental methodology was used and this may cause a slight difference in the obtained results.…”

Section: Comparative Analysis: Ensemble Generation Methodsmentioning

confidence: 99%

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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%

Section: Comparative Analysis: Ensemble Generation Methodsmentioning

confidence: 99%

Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As can be observed, the majority of studies of optimization techniques for the automatic design of ensemble system apply only mono-objective techniques. Nevertheless, some effort has been done to employ multi-objective techniques in the context of ensembles [31][32][33][34]. In [32], for instance, the authors proposed the use of an optimization technique to design ensemble systems, taking into account the accuracy and diversity.…”

Section: State-of-the-art: Optimization Techniques For Classifier Ensmentioning

confidence: 99%

“…However, once again, it uses diversity measures as a guide to select members or features in ensemble systems, not in the multi-objective context. In [33], the authors proposed a framework to obtain ensembles of classifiers from a Multi-objective Evolutionary Algorithm (MOEA), applying two objectives, the Correct Classification Rate or Accuracy (CCR) and the Minimum Sensitivity (MS) of all classes. However, different from this paper, the MOE algorithm was applied to select the classifiers of an ensemble individually, not considering the combination of these classifiers as a whole.…”

Section: State-of-the-art: Optimization Techniques For Classifier Ensmentioning

confidence: 99%

“…Once the comparison with traditional ensemble techniques was conducted, a comparison with recent and promising ensemble techniques was conducted. We selected four ensemble generation techniques: Random Forest (it has largely been used in the ensemble community), XGBoost (a more powerful version of Boosting, proposed in [54]) and P2-SA and P2-WTA [33] (two versions of a multi-objective algorithm for selecting members for an ensemble system).…”

Section: Comparison With Existing Ensemble Generation Techniquesmentioning

confidence: 99%

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Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles

2018

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Metaheuristic algorithms have been applied to a wide range of global optimization problems. Basically, these techniques can be applied to problems in which a good solution must be found, providing imperfect or incomplete knowledge about the optimal solution. However, the concept of combining metaheuristics in an efficient way has emerged recently, in a field called hybridization of metaheuristics or, simply, hybrid metaheuristics. As a result of this, hybrid metaheuristics can be successfully applied in different optimization problems. In this paper, two hybrid metaheuristics, MAMH (Multiagent Metaheuristic Hybridization) and MAGMA (Multiagent Metaheuristic Architecture), are adapted to be applied in the automatic design of ensemble systems, in both mono- and multi-objective versions. To validate the feasibility of these hybrid techniques, we conducted an empirical investigation, performing a comparative analysis between them and traditional metaheuristics as well as existing existing ensemble generation methods. Our findings demonstrate a competitive performance of both techniques, in which a hybrid technique provided the lowest error rate for most of the analyzed objective functions.

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Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network

Ibrahim

Shamsuddin

Abraham

et al. 2019

Neural Comput & Applic

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Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from Multi-objective Evolutionary Algorithms

Cited by 10 publications

References 56 publications

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles

Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network

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