Ensemble Learning Techniques and its Efficiency in Machine Learning: A Survey

N, Thomas. Rincy.; Gupta, Roopam

doi:10.1109/idea49133.2020.9170675

Cited by 68 publications

(26 citation statements)

References 24 publications

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“…The boosting method, which changes weak learners into strong learners by increasing the number of iterations, has a strong theoretical foundation and algorithm characteristics [20,21] . The timing of the method entering the next round of training is determined by the accuracy of classification.…”

Section: Integrated Learning Methodsmentioning

confidence: 99%

Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition

2022

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Named entity recognition (NER) is a fundamental technique in natural language processing that provides preconditions for tasks, such as natural language question reasoning, text matching, and semantic text similarity. Compared to English, the challenge of Chinese NER lies in the noise impact caused by the complex meanings, diverse structures, and ambiguous semantic boundaries of the Chinese language itself. At the same time, compared with specific domains, open-domain entity types are more complex and changeable, and the number of entities is considerably larger. Thus, the task of Chinese NER is more difficult. However, existing open-domain NER methods have low recognition rates. Therefore, this paper proposes a method based on the bidirectional long short-term memory conditional random field (BiLSTM-CRF) model, which leverages integrated learning to improve the efficiency of Chinese NER. Compared with single models, including CRF, BiLSTM-CRF, and gated recurrent unit-CRF, the proposed method can significantly improve the accuracy of open-domain Chinese NER.

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Section: Integrated Learning Methodsmentioning

confidence: 99%

Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition

2022

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“…After that, each division may be used to train a distinct classifier, which can then be merged using a suitable combination algorithm. If there is not enough data, bootstrapping may be used to train alternative classifiers using distinct bootstrap samples of the data, each of which is a random sample of the data taken with replacement and handled as if it were drawn independently from the underlying distribution [48].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach

Khatri¹,

Agrawal²,

Chatterjee³

2022

Scientific Programming

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Recognizing and authenticating wheat varieties is critical for quality evaluation in the grain supply chain, particularly for methods for seed inspection. Recognition and verification of grains are carried out manually through direct visual examination. Automatic categorization techniques based on machine learning and computer vision offered fast and high-throughput solutions. Even yet, categorization remains a complicated process at the varietal level. The paper utilized machine learning approaches for classifying wheat seeds. The seed classification is performed based on 7 physical features: area of wheat, perimeter of wheat, compactness, length of the kernel, width of the kernel, asymmetry coefficient, and kernel groove length. The dataset is collected from the UCI library and has 210 occurrences of wheat kernels. The dataset contains kernels from three wheat varieties Kama, Rosa, and Canadian, with 70 components chosen at random for the experiment. In the first phase, K-nearest neighbor, classification and regression tree, and Gaussian Naïve Bayes algorithms are implemented for classification. The results of these algorithms are compared with the ensemble approach of machine learning. The results reveal that accuracies calculated for KNN, decision, and Naïve Bayes classifiers are 92%, 94%, and 92%, respectively. The highest accuracy of 95% is achieved through the ensemble classifier in which decision is made based on hard voting.

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“…It builds classifiers and combines their output to reduce variances. By mixing the classifiers, ensemble learning improves the accuracy of the task, in comparison to only one classifier [61]. Improving predictive uncertainty evaluation is a difficult task and an ensemble of models can help in this challenge [62].…”

Section: Ensemble Learning Approachesmentioning

confidence: 99%

“…Improving predictive uncertainty evaluation is a difficult task and an ensemble of models can help in this challenge [62]. There are several ensembles approaches, suitable for specific tasks: Dynamic Selection, Sampling Methods, Cost-Sensitive Scheme, Patch-Ensemble Classification, Bagging, Boosting, Adaboost, Random Forest, Random Subspace, Gradient Boosting Machine, Rotation Forest, Deep Neural Decision Forests, Bounding Box Voting, Voting Methods, Mixture of Experts, and Basic Ensemble [61,[63][64][65][66][67][68].…”

Section: Ensemble Learning Approachesmentioning

confidence: 99%

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

et al. 2021

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Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

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Ensemble Learning Techniques and its Efficiency in Machine Learning: A Survey

Cited by 68 publications

References 24 publications

Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition

Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition

Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

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