A Bayesian Approach for Combining Ensembles of GP Classifiers

Stefano, Claudio De; Fontanella, Francesco; Folino, Gianluigi

doi:10.1007/978-3-642-21557-5_5

Cited by 28 publications

(6 citation statements)

References 9 publications

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“…A new Bayesian approach to combining classifiers was presented by De Stefano et al . (2011). In their work, they applied a Bayesian Network (BN) to compute the joint probability: .…”

Section: Combining Methodsmentioning

confidence: 99%

A survey of commonly used ensemble-based classification techniques

Jurek¹,

Bi²,

Wu³

et al. 2013

The Knowledge Engineering Review

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The combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts, which have been made to improve their performance. Within this paper, we present and compare an updated view on the different modifications of these techniques, which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting. In addition, we provide a review of different ensemble selection methods based on both static and dynamic approaches. We present some new directions which have been adopted in the area of classifier ensembles from a range of recently published studies. In order to provide a deeper insight into the ensembles themselves a range of existing theoretical studies have been reviewed in the paper.

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“…A new Bayesian approach to combining classifiers was presented by De Stefano et al . (2011). In their work, they applied a Bayesian Network (BN) to compute the joint probability: .…”

Section: Combining Methodsmentioning

confidence: 99%

A survey of commonly used ensemble-based classification techniques

Jurek¹,

Bi²,

Wu³

et al. 2013

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…These aspects will be the subject of our future research activity. Future work will also include: (i) a further analysis of these results, which will involve doctors specialized in brain disease and dementia; (ii) more feature selection techniques [45], [46]; (iii) the development of classification systems based on the combination of the predictions provided by the classifiers trained on the data from the single tasks [47]- [50].…”

Section: Discussionmentioning

confidence: 99%

Feature Selection as a Tool to Support the Diagnosis of Cognitive Impairments Through Handwriting Analysis

2021

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Cognitive Impairments are cognitive deficits that are greater than expected for a person of a given age and level of education, but which do not significantly interfere with the daily life of the people affected. They range from mild to severe and are seen as a risk factor for Alzheimer's disease, currently the most common neurodegenerative brain disorder worldwide. In a previous study, we presented an experimental protocol comprising different handwriting tasks to be carried out by patients and a healthy control group: the aim was to investigate whether the analysis of the handwriting could be used as a tool to support the diagnosis of this kind of impairment. In the study presented here, we used a wellknown and widely-used feature selection approach to determine the most effective features for predicting the symptoms related to cognitive impairments via handwriting analysis. Our intention is to deepen the knowledge about the different cognitive functions affected by the onset of these diseases, as well as to improve the performance of the tools developed to support their diagnosis. The results showed that different sets of highly discriminant features, closely related to the cognitive skills impaired, were selected for the handwriting tasks making up the protocol, thus supporting our hypothesis that their use can be very helpful to support the diagnosis of cognitive impairment. INDEX TERMSMedical expert systems, Cognitive impairments, Feature selection.

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“…Several researchers defend diversity as a valid objective (e.g., Folino et al, 2006;Stefano et al, 2011;Chiu & Verma, 2013), stating that it contributes to ensemble accuracy (Chiu & Verma, 2013). De Stefano et al (2011) state that, as the number of base learners increase, so does the probability that a minority of correct base learners will be overrun by a majority of wrong base learners, and thus the need for using diversity measures to reverse that effect. Also, in EAs, genetic material from well-performing solutions tend to be propagated to their offspring, often compromising diversity (Duell et al, 2006).…”

Section: Effectiveness Diversity Complexity and Efficiencymentioning

confidence: 99%

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

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This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

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A Bayesian Approach for Combining Ensembles of GP Classifiers

Cited by 28 publications

References 9 publications

A survey of commonly used ensemble-based classification techniques

A survey of commonly used ensemble-based classification techniques

Feature Selection as a Tool to Support the Diagnosis of Cognitive Impairments Through Handwriting Analysis

A survey of evolutionary algorithms for supervised ensemble learning

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