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Error-Sensitive Grading for Model Combination [chapter]

Surendra K. Singhi, Huan Liu
2005 Lecture Notes in Computer Science  
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]

Ling Liu, Wenqi Wei, Ka-Ho Chow, Margaret Loper, Emre Gursoy, Stacey Truex, Yanzhao Wu
2019 arXiv   pre-print
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

Dhimant Ganatra
2020 International Journal of Advanced Trends in Computer Science and Engineering  
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

Tu Peng, Xiaoya Chen, Ming Wan, Lizhu Jin, Xiaofeng Wang, Xuejie Du, Hui Ge, Xu Yang
2020 International Journal of Environmental Research and Public Health  
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

Peyman Sheikholharam Mashhadi, Sławomir Nowaczyk, Sepideh Pashami
2021 Neural Networks  
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

Jogendra SinghKushwah, Divakar Singh
2013 International Journal of Computer Applications  
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

Dhimant Ganatra
2020 International Journal of Emerging Trends in Engineering Research  
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

Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, Kimito Funatsu
2020 Journal of Cheminformatics  
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]

Xinlei Zhou and Han Liu and Farhad Pourpanah and Tieyong Zeng and Xizhao Wang
2021 arXiv   pre-print
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

Lean Yu, Shouyang Wang, Kin Keung Lai
2008 Journal of Systems Science and Complexity  
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

Muhammad Pervez Akhter, Jiangbin Zheng, Farkhanda Afzal, Hui Lin, Saleem Riaz, Atif Mehmood
2021 PeerJ Computer Science  
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

Rui Shi, Chunmao Jiang
2022 IEEE Access  
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

Isaac Kofi Nti, Owusu Narko-Boateng, Adebayo Felix Adekoya, Arjun Remadevi Somanathan
2022 ˜The œinternational Arab journal of information technology  
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

Zhibin Zhao, Jianfeng Xu, Yanlong Zang, Ran Hu
2021 Applied Sciences  
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

Gulshan Kumar, Krishan Kumar
2012 Applied Computational Intelligence and Soft Computing  
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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