Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods&lt;strong&gt; &lt;/strong&gt;

Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints201908.0203.v1

Cited by 40 publications

(7 citation statements)

References 76 publications

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“…Hybrid models are popular nowadays as they combine the output of multiple models and produce more accurate results compared to single models Ardabili et al (2019). The fundamental principle underlying hybrid modelling is to combine the outputs of various models in order to take advantage of their strengths and minimise their flaws, thus improving the robustness and accuracy of predictions Zhang et al (2019); .…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance

Wilson,

Nehme,

Dhyani

et al. 2024

Risks

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This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of “technical models” which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected amount per claim). Technical models are designed to predict the loss costs (the product of frequency and severity, i.e., the expected claim amount per unit of exposure) and this is a main factor that is taken into account for pricing insurance policies. Other factors for pricing include the company expenses, investments, reinsurance, underwriting, and other regulatory restrictions. Different machine learning methodologies, including the Generalised Linear Model (GLM), Gradient Boosting Machine (GBM), Artificial Neural Networks (ANN), and a unique hybrid model that combines GLM and ANN, were explored for creating the technical models. This study was conducted on the French Motor Third Party Liability datasets, “freMTPL2freq” and “freMTPL2sev” included in the R package CASdatasets. After building the aforementioned models, they were evaluated and it was observed that the hybrid model which combines GLM and ANN outperformed all other models. ANN also demonstrated better predictions closely aligning with the performance of the hybrid model. The better performance of neural network models points to the need for actuarial science and the insurance industry to look beyond traditional modelling methodologies like GLM.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance

Wilson,

Nehme,

Dhyani

et al. 2024

Risks

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“…In machine learning, the term hybrid model is mainly used to describe models that use different machine learning algorithms of different principles (Ardabili et al, 2019). As an example, it is possible to cite the union of algorithms based on decision trees with neural networks, algorithms based on supervised and unsupervised learning and workflows that use classical machine learning with deep learning.…”

Section: Hybrid Modelsmentioning

confidence: 99%

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Oliveira

Freitas

Pesce

et al. 2023

Geophysical Prospecting

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Well mineralogy can be estimated from probabilistic, direct and machine learning models; however, all these models have limitations. The maximum number of components in probabilistic models is restricted to the number of logs plus one. Direct models require the precise composition of minerals. Machine learning models demand unbiased databases, a challenge as the samples are collected in reservoir intervals. These limitations impact the evaluation for the Santos Basin pre‐salt rocks due to the complexity of facies and magnesian clays. This work proposes creating a hybrid model through the combination of probabilistic and machine learning models. First, mineral fractions of calcite, dolomite, quartz, k‐feldspar, detrital clay, plagioclase and pyroxene are estimated by the algorithm XGBoost trained using rock samples. Then, a probabilistic model reconstructs the well logs and machine learning estimations through the seven minerals mentioned plus magnesian clays, pyrite, barite and fluids. The difference between the real and reconstructed responses is minimized, weighted by the curves’ uncertainties. The hybrid model is used to estimate the mineralogy of three wells drilled in the Santos Basin, honouring the mineralogy of the rock samples collected in these wells and improving the quantification of dolomite, pyroxene and magnesian clay. Among the advances introduced, the following stand out: The use of machine learning estimates and well logs improved the quantification of magnesian clay; the machine learning estimates regularized the probabilistic model, generating more coherent results; the uncertainties of the machine learning algorithms dealt with database bias. The hybrid model mitigated limitations related to database bias without the costs associated with collecting more samples.

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“…Different training algorithms benefit from ensemble approaches, which increase the training accuracy to raise the testing accuracy. The ensemble approach may use different training algorithms to provide flexible training [14].…”

Section: Ensemble Machine Learningmentioning

confidence: 99%

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

A. Kadheem Hammood,

T. Sadiq

2023

ijci

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The rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset.

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Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Cited by 40 publications

References 76 publications

A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance

A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

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