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Error-Sensitive Grading for Model Combination
[chapter]
2005
Lecture Notes in Computer Science
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An important decision while using an ensemble of classifiers is to decide upon a way of combining the prediction of its base classifiers. ...
This method distinguishes between the grading error of classifying an incorrect prediction as correct, and the other-way-round, and tries to assign appropriate costs to the two types of error in order ...
One way to determine this costratio is using cross-validation. It helps dynamically adjust the cost depending upon the number and diversity of base classifiers in an ensemble. ...
doi:10.1007/11564096_74
fatcat:vzhntm2gebfx5mgzg72en2zblq
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
[article]
2019
arXiv
pre-print
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Another attractive property of diversity optimized ensemble learning is its robustness against deception: an adversarial perturbation attack can mislead one DNN model to misclassify but may not fool other ...
In this paper we first give an overview of the concept of ensemble diversity and examine the three types of ensemble diversity in the context of DNN classifiers. ...
This work is partially sponsored by NSF CISE SaTC grant 1564097 and an IBM faculty award. ...
arXiv:1908.11091v1
fatcat:oo7wgjsupbhdno577qirsosbr4
Ensemble Methods to Improve Accuracy of a Classifier
2020
International Journal of Advanced Trends in Computer Science and Engineering
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A decision tree algorithm is developed for a B-School which can be used as a strategy to identify potential placeable candidates at the time of admission itself based on their past academic performance ...
The disadvantage of a single tree can be overcome if there is an approach which can produce multiple trees and combine them to yield a better prediction. ...
An ensemble tree based model classifier technique for predicting the student performance was used by [10] . ...
doi:10.30534/ijatcse/2020/145932020
fatcat:wray6phoq5coxodjimerfweyya
The Prediction of Hepatitis E through Ensemble Learning
2020
International Journal of Environmental Research and Public Health
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Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random ...
Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical ...
The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/ijerph18010159
pmid:33379298
pmcid:PMC7795791
fatcat:zbidgg4iujccbmsvzjrpgvap4e
Parallel orthogonal deep neural network
2021
Neural Networks
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It is the diversity of the models within an ensemble that allows the ensemble to correct the errors made by its members, and consequently leads to higher classification or regression performance. ...
A mistake made by a base model can only be rectified if other members behave differently on that particular instance, and provide the aggregator with enough information to make an informed decision. ...
Acknowledgments This work was supported by the KK-stiftelsen, Sweden ; and Vinnova, Sweden . ...
doi:10.1016/j.neunet.2021.03.002
pmid:33765532
fatcat:jj7xatiq2zewdoiy455xjnudgu
A Comparative Result Analysis of Human Cancer Diagnosis using Ensemble Classification Methods
2013
International Journal of Computer Applications
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cost extra processing time and higher storage as decision tree or neural network are faster as compared to ensemble based techniques. ...
In this paper ensemble based classification methods which combine the prediction of individual classifiers to generate the final prediction are discussed. ...
Evaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model, so ensemble may be thought of as a way to compensate for poor learning algorithms ...
doi:10.5120/13373-0977
fatcat:sc4oezcnwreljlfe55lovujtfq
The Sensitivity Conundrum – Random Forest or Boosting
2020
International Journal of Emerging Trends in Engineering Research
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Ensemble methods like random forest and boosting combine predictions from multiple models into one that is far superior to the individual models. ...
The True Positive Rate varies based on the type of ensemble used among other factors. ...
The top 10 performing models have an OOB error between 11.80% and 13.04%which is lower than the default or the three manually tuned models. ...
doi:10.30534/ijeter/2020/56872020
fatcat:frmxpxtksbcgvbs6qqdf4ngieq
Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
2020
Journal of Cheminformatics
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Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. ...
In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting ...
It is called "ensemble learning. " Random forests (RF) is one of the examples of decision tree (DT) based ensemble learning models [9] . ...
doi:10.1186/s13321-020-0417-9
pmid:33430997
fatcat:7xbmhvnknreuze3guoytbabp7m
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
[article]
2021
arXiv
pre-print
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Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. ...
., Bayesian and ensemble. This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years. ...
Acknowledgment This work was supported in part by the National Natural Science Foundation of China (Grants 61976141, 62176160 and 61732011), in part by National Key R&D ...
arXiv:2111.01968v2
fatcat:rdyhcib3ebgmdfjmh5e43eu2pi
Forecasting China's Foreign Trade Volume with a Kernel-Based Hybrid Econometric-Ai Ensemble Learning Approach
2008
Journal of Systems Science and Complexity
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In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables ...
To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign ...
But in foreign trade forecasting, improved decisions often depend on correct forecasting directions between the actual and predicted values, y t and y t , respectively. ...
doi:10.1007/s11424-008-9062-5
fatcat:7uvex46cnjbe3n6xltnjbwhx4e
Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media
2021
PeerJ Computer Science
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Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning ...
Third, we use five ensemble learning methods to ensemble the base-predictors' predictions to improve the fake news detection system's overall performance. ...
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ...
doi:10.7717/peerj-cs.425
pmid:33817059
pmcid:PMC7959660
fatcat:c6mqbz45aregddjkivfuzzes3a
Three-Way Ensemble Prediction for Workload in the Data Center
2022
IEEE Access
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Then, according to the basic idea of the three-way decision, we assigned various prediction models based on workload characteristics and a priori error prediction to improve the prediction accuracy further ...
INDEX TERMS Three-way decision, three-way division, cost assessment, load prediction, cloud energy consumption. ...
PREDICTION MODEL BASED ON THREE-WAY DECISION An ensemble approach involves the use of multiple prediction models to predict the estimated future outcome and each of them is commonly referred to as a base ...
doi:10.1109/access.2022.3145426
fatcat:djh2x52lvrc63osc4f3mtaprc4
Stacknet based decision fusion classifier for network intrusion detection
2022
˜The œinternational Arab journal of information technology
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We compare our proposed model with five other ensemble models, a deep neural model and two stand-alone state-of-the-art classifiers commonly used in network intrusion detection based on accuracy, the Area ...
Specifically, the proposed model sought to improve the association between the prediction accuracy and diversity of base classifiers. ...
Acknowledgment We are grateful to all who supported this study in the computer science and informatics departments at UENR. ...
doi:10.34028/iajit/19/3a/8
dblp:journals/iajit/NtiNAS22
fatcat:rzx6covx7rfc3kj3fo5f43wwsy
Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
2021
Applied Sciences
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At the same time, based on the concept drift detection strategy and Adaboost ensemble learning methods, adaptive update of the base classifier in the abnormal diagnosis model was realized. ...
First, the bagging ensemble learning method was used to predict the oil temperature. ...
Acknowledgments: We are grateful for the National Natural Science Foundation of China and the Jiangxi Provincial Natural Science Foundation. ...
doi:10.3390/app11146322
fatcat:dj4qnifljjgr3o2p44iuikdd6e
The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review
2012
Applied Computational Intelligence and Soft Computing
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Here, an updated review of ensembles and their taxonomies has been presented in general. ...
In supervised learning-based classification, ensembles have been successfully employed to different application domains. ...
This level focuses on ensemble integration phase of ensemble learning process. Here, predictions of base classifiers are combined in some way to improve the performance of ensemble. ...
doi:10.1155/2012/850160
fatcat:rxi5t7appjgl3pn2l7bbb5ru3q
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