A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Mienye, Ibomoiye Domor; Sun, Yanxia

doi:10.1109/access.2022.3207287

Cited by 358 publications

(114 citation statements)

References 188 publications

(193 reference statements)

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“…Unlike bagging and boosting, which uses combination rules such as majority voting and weighted majority voting, the stacking ensemble model uses another ML algorithm (i.e., meta-learner) to aggregate the predictions from the level-0 models [72]. In the literature, stacking-based ensembles have been applied in diverse fields [73]- [75].…”

Section: E Ensemble Learningmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

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Section: E Ensemble Learningmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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“…Having selected the training samples, training the classifier is rather straightforward. The XGB framework been selected because of its showcased competence in achieving high accuracy in different classification and regression tasks [22], including very recently in wildfire research [35] [36] [37] (in quite different tasks than our work). Perhaps most importantly, the XGBoost open-source software library [21] is extremely optimized in terms of parallel execution and memory minimization, being able to train an extremely deep set of trees using more than 60,000 training patterns (as we selected for the experiments) in a few seconds.…”

Section: Training and Applying The Xgb Classifiermentioning

confidence: 99%

“…The proposed methodology has been designed to be applicable in a nationwide operational framework, with the classification models being trained considering a database of 64 reference fire perimeters in Greece from 2016 to 2019. For the supervised classification step, the extreme Gradient Boosting (XGB) [21] machine learning framework has been selected, which the last few years showcases very high competence in achieving high accuracy in different classification and regression tasks [22]. An empirical approach for selecting representative training patterns via difference spectral indices is also proposed, which is a crucial element and guarantees the effectiveness of the whole methodology.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

Stavrakoudis¹,

Gitas²

2023

JGIS

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The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.

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“…Authors in [26] proposed TF-IDF (Term Frequency-Inverse Document Frequency) and BM25 [27] based information extraction methods along with a ranking method inspired by [28] over LDA and NMF topic modeling. Authors in [29] proposed an ensemble learning [30] based methodology over truth discovery algorithm [31] that consists of a graph with topics, words, Wikipedia articles, and candidate terms as nodes, and relationship among them as edge. Authors in [32] applied graph extraction methods over DBpedia 1 concepts.…”

Section: B Graph-based Approachesmentioning

confidence: 99%

Advanced Hierarchical Topic Labeling for Short Text

et al. 2023

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Hierarchical Topic Modeling is the probabilistic approach for discovering latent topics distributed hierarchically among the documents. The distributed topics are represented with the respective topic terms. An unambiguous conclusion from the topic term distribution is a challenge for readers. The hierarchical topic labeling eases the challenge by facilitating an individual, appropriate label for each topic at every level. In this work, we propose a BERT-embedding inspired methodology for labeling hierarchical topics in short text corpora. The short texts have gained significant popularity on multiple platforms in diverse domains. The limited information available in the short text makes it difficult to deal with. In our work, we have used three diverse short text datasets that include both structured and unstructured instances. Such diversity ensures the broad application scope of this work. Considering the relevancy factor of the labels, the proposed methodology has been compared against both automatic and human annotators. Our proposed methodology outperformed the benchmark with an average score of 0.4185, 49.50, and 49.16 for cosine similarity, exact match, and partial match, respectively.

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A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Cited by 358 publications

References 188 publications

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

Advanced Hierarchical Topic Labeling for Short Text

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