A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

Onan, Aytuğ; Korukoğlu, Serdar; Bulut, Hasan

doi:10.1016/j.eswa.2016.06.005

Cited by 327 publications

(183 citation statements)

References 64 publications

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“…As 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.…”

Section: Recent Studies In Weighted Combination Methods For In Classimentioning

confidence: 99%

“…One way to improve the efficiency of combination methods is through the use of weights that can be used to denote the confidence (influence) of the individual classifiers in classifying an input pattern to a particular class [47]. Different ways of calculating weights (confidence) of each class for each individual classifier can be used in determining the relative contribution of each classifier within a classifier ensemble and they can be classified as static [39,45,46,50,51,57,70] or dynamic weighting [4,45,51,57]. For offering more flexibility and efficiency, in this paper, we will be working with dynamic weight selection (dynamic weighting).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

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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: Recent Studies In Weighted Combination Methods For In Classimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Combining multiple algorithms in classifier ensembles using generalized mixture functions

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“…Studies considering the use of evolutionary algorithms for optimizing the polarity values of opinion concepts have been proposed only recently (Ferreira et al, 2015;Onan et al, 2016Onan et al, , 2017. However, these works focused on learning candidate refinements of opinion concepts polarity without considering the context dimension associated with them.…”

Section: Related Workmentioning

confidence: 99%

NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text

Dragoni

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes the NeuroSent system that participated in SemEval 2018 Task 1. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.

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“…(Balahur, 2013;Martínez-Cámara et al, 2014;Balikas and Amini, 2016;Onan et al, 2016). A voting scheme combines learning algorithms to identify and select an optimal set of base learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

Symeonidis¹,

Effrosynidis²,

Kordonis³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

Cited by 327 publications

References 64 publications

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Combining multiple algorithms in classifier ensembles using generalized mixture functions

NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

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