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Ensemble Methods
Ensemble ML methods build predictive models by inducing several different predictors, called base predictors or base learners. Typically, a base...
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Margin distribution and structural diversity guided ensemble pruning
Ensemble methods that train and combine multiple learners have always been among the state-of-the-art learning methods, and ensemble pruning aims at...
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Diversified deep hierarchical kernel ensemble regression
Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be...
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A geometric framework for multiclass ensemble classifiers
Ensemble classifiers have been investigated by many in the artificial intelligence and machine learning community. Majority voting and weighted...
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Performance optimization in ddos prediction with ensemble based approach
Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential...
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Adversarial-Based Ensemble Feature Knowledge Distillation
Existing knowledge distillation methods usually consider class probabilities or directly align features, which ignore the rich information contained...
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Blockchain transaction deanonymization using ensemble learning
Bitcoin is a digital currency that provides a way to transact without any trusted intermediary; however, privacy is an issue. Numerous...
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Evaluating ensemble imputation in software effort estimation
Choosing the appropriate missing data (MD) imputation technique for a given software development effort estimation (SDEE) technique is not a trivial...
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A new ensemble method for brain tumor segmentation
Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are crucial and challenging tasks for several applications in the...
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An ensemble learning interpretation of geometric semantic genetic programming
Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to...
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Dynamic weighted ensemble for diarrhoea incidence predictions
Diarrhoea (DH) disease pose significant threats to national morbidity and mortality in Vietnam, especially on children. Being a climate sensitive...
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Weighted ensemble CNN for lung nodule classification: an evolutionary approach
Lung Cancer is the deadliest cancer with maximum mortality rates all over the world. If lung cancers are not detected at an early stage, they will...
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Ensemble Learning
The fundamental idea behind ensemble learning is to create a robust and accurate predictive model by combining predictions of multiple simpler...
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Evolutionary Ensemble Learning
Evolutionary Ensemble Learning (EEL) provides a general approach for scaling evolutionary learning algorithms to increasingly complex tasks. This is...
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A comprehensive ensemble pruning framework based on dual-objective maximization trade-off
Ensemble learning has gotten a lot of interest because of its capacity to increase predictive accuracy by merging numerous models. However, redundant...
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Ensemble learning with weighted voting classifier for melanoma diagnosis
Melanoma, the most lethal type of skin cancer, presents a substantial public health challenge. Detecting melanoma promptly is paramount for enhancing...
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PCS-granularity weighted ensemble clustering via Co-association matrix
Ensemble clustering has attracted much attention for its robustness and effectiveness compared to single clustering. As one of the representative...
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PANACEA : a neural model ensemble for cyber-threat detectionEnsemble learning is a strategy commonly used to fuse different base models by creating a model ensemble that is expected more accurate on unseen...
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Multi-language: ensemble learning-based speech emotion recognition
Inaccurate emotional reactions from robots have been a problem for authors in previous years. Since technology has advanced, robots like service...
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Automated machine learning with dynamic ensemble selection
Automated machine learning (AutoML) has been developed for automatically building effective machine learning pipelines. However, existing AutoML...
