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Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems [article]

Mohsen Shahhosseini, Guiping Hu, Hieu Pham
2020 arXiv   pre-print
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately.  ...  It is known that tuning hyperparameters of each base learner inside the ensemble weight optimization process can produce better performing ensembles.  ...  It is obvious that the base first level learners can be any combination of machine learning models.  ... 
arXiv:1908.05287v6 fatcat:byfi3zpksfgglmm4hnhxdizsua

Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines [article]

Andrei V. Konstantinov, Lev V. Utkin
2020 arXiv   pre-print
The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on a single feature and produces a shape function of the feature.  ...  The ensemble is composed as a weighted sum of separate GBMs resulting a weighted sum of shape functions which form the generalized additive model.  ...  This observation again illustrates the strength of the GBM as a machine learning model.  ... 
arXiv:2010.07388v1 fatcat:hkuaho5zxfed5hoipzpcyzdyz4

Majority vote ensembles of conformal predictors

Giovanni Cherubin
2018 Machine Learning  
We study majority vote ensembles of ε-valid conformal predictors (CP).  ...  We further indicate an error upper bound for an ensemble of correlated CPs, and derive a value ε for which such an ensemble guarantees η conservative validity.  ...  The author was supported by the EPSRC and the UK government as part of the Centre for Doctoral Training in Cyber Security at Royal Holloway, University of London (EP/K035584/1).  ... 
doi:10.1007/s10994-018-5752-y fatcat:23gsvtco7jfxbbqye5eqfuhivy

Estimate the Warfarin Dose by Ensemble of Machine Learning Algorithms [article]

Zhiyuan Ma, Ping Wang, Zehui Gao, Ruobing Wang, Koroush Khalighi
2018 arXiv   pre-print
Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine  ...  Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1.  ...  One way to overcome the limitation of a single algorithm is to combine the advantages of several algorithms to break through the upper limit of a single machine learning algorithm (i.e. ensemble method  ... 
arXiv:1809.04069v2 fatcat:cmjhqjdi3rad5hz5nx5d7nkfxm

Machine learning in economic planning: ensembles of algorithms

J An, A Y Mikhaylov, N E Sokolinskaya
2019 Journal of Physics, Conference Series  
measurements, the proposed algorithm for machine learning model leads to a fairly rapid convergence of estimates.  ...  The algorithm for machine learning of a transport type model is presented for the optimal distribution of tasks in safety critical systems operating in an automatic mode without operator participation.  ...  Introduction Machine learning algorithms are used in several fields besides computer science, including critical systems. Learning is the process of acquiring knowledge.  ... 
doi:10.1088/1742-6596/1353/1/012126 fatcat:vt7xhjup6bbazkwlcomkh2qjli

Characterizing Ensembles of Platelike Particles via Machine Learning

Anna Jaeggi, Ashwin Kumar Rajagopalan, Manfred Morari, Marco Mazzotti
2020 Industrial & Engineering Chemistry Research  
The second approach uses a machine learning model to estimate the three lengths from two projections (method 3).  ...  Several machine learning models are identified and optimized to predict the three particle lengths based on the two projections from the imaging device.  ...  Choice of Machine Learning Model.  ... 
doi:10.1021/acs.iecr.0c04662 fatcat:zefcyubrcvfn3l4wwf4klltiiq

Ensemble Machine Learning Based Identification of Pediatric Epilepsy

Shamsah Majed Alotaibi, Atta-ur-Rahman, Mohammed Imran Basheer, Muhammad Adnan Khan
2021 Computers Materials & Continua  
A group of Naïve Bayes (NB), Support vector machine (SVM), Logistic regression (LR), k-nearest neighbor (KNN), Linear discernment (LD), Decision tree (DT), and ensemble learning methods were applied to  ...  This paper investigates the detection of epileptic seizures by applying supervised machine learning techniques.  ...  In addition, further advanced algorithms could be applied, including deep learning and extreme learning machines, etc.  ... 
doi:10.32604/cmc.2021.015976 fatcat:5ajftstikncx3cf6la642bjicu

Ensemble of machine learning algorithms for intrusion detection

Te-Shun Chou, Jeffrey Fan, Sharon Fan, Kia Makki
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
Ensemble-classifier is a technique that uses a combination of multiple classifiers to reach a more precise inference result than a single classifier.  ...  In addition, the performances of a variety of combination methods that fuse the outputs from classifiers are studied.  ...  In each base feature selecting classifier, we apply different machine learning algorithm and feature subset to solve uncertainty problem and maximize the diversity.  ... 
doi:10.1109/icsmc.2009.5346669 dblp:conf/smc/ChouFFM09 fatcat:lbsn7rt5enf7hjfl3hsthbjm7m

A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions [article]

Liming Fu, Peng Liang, Xueying Li, Chen Yang
2021 arXiv   pre-print
Especially, we applied an ensemble learning method and constructed ensemble classifiers to compare the performance between ensemble classifiers and base classifiers.  ...  We then experimented and evaluated 270 configurations regarding feature selection, feature extraction techniques, and machine learning classifiers to seek the best configuration for classifying decisions  ...  ACKNOWLEDGMENTS This work has been partially supported by the National Key R&D Program of China with Grant No. 2018YFB1402800.  ... 
arXiv:2105.01011v1 fatcat:nwaudeitkzdlhfh2cwrz43ljta

Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems [article]

Razieh Baradaran, Hossein Amirkhani
2021 arXiv   pre-print
In this paper, we investigate the effect of ensemble learning approach to improve generalization of MRC systems without retraining a big model.  ...  We identify the important factors in the effectiveness of ensemble methods. Also, we compare the robustness of ensemble and fine-tuned models against data distribution shifts.  ...  Ensemble learning for improving generalization Ensemble learning is a set of methods that combine multiple base learners to make a better decision [39] .  ... 
arXiv:2107.00368v2 fatcat:bebo4asik5hu3ekeapd7iqoypq

Building heterogeneous ensembles by pooling homogeneous ensembles

Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
2021 International Journal of Machine Learning and Cybernetics  
In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T.  ...  AbstractHeterogeneous ensembles consist of predictors of different types, which are likely to have different biases.  ...  learns in a heterogeneous ensemble.  ... 
doi:10.1007/s13042-021-01442-1 fatcat:tmxia7w7bfhtzg7mg7u7fmrw4q

Dimension Reduction Using Rule Ensemble Machine Learning Methods: A Numerical Study of Three Ensemble Methods [article]

Orianna DeMasi, Juan Meza, David H. Bailey
2011 arXiv   pre-print
Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight "base learners."  ...  We consider an ensemble technique that returns a model of ranked rules.  ...  Comparing the time efficiency of the rule ensemble with other tree methods and other machine learning techniques will be part of future work.  ... 
arXiv:1108.6094v1 fatcat:xoo6k5q4arefjinhwa4ijcnihm

Comparing machine learning and ensemble learning in the field of football

Shuaib Khan, Kirubanand V. B
2019 International Journal of Electrical and Computer Engineering (IJECE)  
This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions.  ...  Predicting football matches results seems like the perfect problem for machine learning models.  ...  ACKNOWLEDGEMENTS The author would like to thank facilitators of the university have been a most helpful ally in structuring the data and understanding the problem domain.  ... 
doi:10.11591/ijece.v9i5.pp4321-4325 fatcat:biheixhm2fdqjgbywqfcsp6dcy

Ensemble machine learning approach for classification of IoT devices in smart home

Ivan Cvitić, Dragan Peraković, Marko Periša, Brij Gupta
2021 International Journal of Machine Learning and Cybernetics  
This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model.  ...  AbstractThe emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities  ...  For the model development, the ensemble supervised machine learning method was used.  ... 
doi:10.1007/s13042-020-01241-0 fatcat:dxjmedzo4nchffho4lzbiugw7e

Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation [article]

Jan Weinreich, Nicholas J. Browning, O. Anatole von Lilienfeld
2021 arXiv   pre-print
We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling  ...  of heavy atoms.  ...  In the following we refer to KRR using a given vacuum geometry as QML and to free energy machine learning using the ensemble averaged FCHL19 representation as FML.  ... 
arXiv:2012.09722v5 fatcat:w2z4xcq335htxaxhkd4y7la26q
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