An exploratory study of mono and multi-objective metaheuristics to ensemble of classifiers

Neto, Antonino A. Feitosa; Canuto, Anne M. P.

doi:10.1007/s10489-017-0982-4

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…This observation led to an alternative approach to improve the performance of a classifier, which consists of combining several different classifiers (that is, an ensemble of them) and taking the class proposed by their combination. Over the years, many researchers have studied methods for constructing good ensembles of classifiers [16,22,30,32,42,46], showing that indeed ensemble classifiers are often much more accurate than the individual classifiers within the ensemble [30]. Classifiers combination is widely applied to many different fields, such as urban environment classification [3,53] and medical decision support [2,49].…”

Section: Introductionmentioning

confidence: 99%

Voting with random classifiers (VORACE): theoretical and experimental analysis

Cornelio

Donini

Loreggia

et al. 2021

Auton Agent Multi-Agent Syst

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In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

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Section: Introductionmentioning

confidence: 99%

Voting with random classifiers (VORACE): theoretical and experimental analysis

Cornelio

Donini

Loreggia

et al. 2021

Auton Agent Multi-Agent Syst

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“…As a result of this study, the authors stated that the choice of the optimization technique is an objective-dependent selection and no single technique is the best selection for all cases (mono-and multi-objective as well as datasets). The results obtained in [15] are the main motivation for the investigation performed here.…”

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

confidence: 84%

“…The definition of the best technique for a particular classification application is a challenging problem and has been addressed by several authors [15][16][17][18][19][20][21]. This problem has been treated as a meta-learning problem [17,22], automatic selection of machine learning (auto-ML) [16,18,20] or an optimization problem [15,19,21]. In [18], for instance, the authors used an auto-ML technique to define the best classification algorithm, along with the best set of parameters, for a specific classification problem.…”

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

confidence: 99%

“…In either the mono-or multi-objective context, to the best of the authors knowledge, there is no study using hybrid metaheuristics in the optimization of ensemble systems. In [15], for instance, an exploratory study with several optimization techniques in the design of ensemble systems is presented. In the presented study, the authors investigated several techniques in both mono-and multi-objective versions.…”

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

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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“…In Machine Learning, the choice of the best technique for a particular classification problem is a challenge which has been addressed by several authors [25]- [31]. This problem has been treated as a meta-learning problem [27], [32], automatic selection of machine learning (auto-ML) [26], [28], [30] or an optimization problem [25], [29], [31].…”

Section: Related Workmentioning

confidence: 99%

Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning

et al. 2021

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Machine Learning (ML) is a field that aims to develop efficient techniques to provide intelligent decision making solutions to complex real problems. Among the different ML structures, a classifier ensemble has been successfully applied to several classification domains. A classifier ensemble is composed of a set of classifiers (specialists) organized in a parallel way, and it is able to produce a combined decision for an input pattern (instance). Although Classifier ensembles have proved to be robust in several applications, an important issue is always brought to attention is the ensemble's structure. In other words, the correction definition of its structure, like the number and type of classifiers and the aggregation method, has an important role in its performance. Usually, an exhaustive testing and evaluation process is required to better define the ideal structure for an ensemble. Aiming to produce an interesting investigation in this field, this paper proposes two new approaches for automatic recommendation of classifier ensemble structure, using meta-learning to recommend three of these important parameters: type of classifier, number of base classifiers, and the aggregation method. The main aim is to provide a robust structure in a simple and fast way. In this analysis, five well known classification algorithms will be used as base classifiers of the ensemble: kNN (Nearest Neighbors), DT (Decision Tree), RF (Random Forest), NB (Naive Bayes) e LR (Logistic Regression). Additionally, the classifier ensembles will be evaluated using seven different strategies as aggregation functions: HV (Hard Voting), SV (Soft Voting), LR (Logistic Regression), SVM (Support Vector Machine), NB(Naive Bayes), MLP (Multilayer perceptron) e DT (Decision Tree). The empirical analysis shows that our approach can lead to robust classifier ensembles, for the majority of the analysed cases.INDEX TERMS Classifier ensembles, Meta-learning, Multiple classifier system, Machine Learning.

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An exploratory study of mono and multi-objective metaheuristics to ensemble of classifiers

Cited by 11 publications

References 26 publications

Voting with random classifiers (VORACE): theoretical and experimental analysis

Voting with random classifiers (VORACE): theoretical and experimental analysis

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

Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning

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