Journal articles: 'Ensemble learning methods'

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Qutub, Aseel, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi. "Prediction of Employee Attrition Using Machine Learning and Ensemble Methods." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 110–14. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1022.

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Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, Adaboost Model, and Gradient Boosting Classifier models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.

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Abdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, and Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (April 26, 2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.

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Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering

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Zhang, Boyu, Ji Xiang, and Xin Wang. "Network representation learning with ensemble methods." Neurocomputing 380 (March 2020): 141–49. http://dx.doi.org/10.1016/j.neucom.2019.10.098.

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Tolmidis, Avraam Th, and Loukas Petrou. "Ensemble Methods for Cooperative Robotic Learning." International Journal of Intelligent Systems 32, no. 5 (October 26, 2016): 502–25. http://dx.doi.org/10.1002/int.21858.

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Evangelista, Edmund De Leon, and Benedict Descargar Sy. "An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5226. http://dx.doi.org/10.11591/ijece.v12i5.pp5226-5235.

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<span lang="EN-US">Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naïve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict students’ performance. The comparative analysis of the model’s accuracy showed that the best-performing single classifier’s accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods.</span>

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TURAN, SELIN CEREN, and MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS." Journal of Science and Arts 22, no. 2 (June 30, 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.

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Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared

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Alhazmi, Omar H., and Mohammed Zubair Khan. "Software Effort Prediction Using Ensemble Learning Methods." Journal of Software Engineering and Applications 13, no. 07 (2020): 143–60. http://dx.doi.org/10.4236/jsea.2020.137010.

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Liu, Fayao, Ruizhi Qiao, Chunhua Shen, and Lei Luo. "Designing ensemble learning algorithms using kernel methods." International Journal of Machine Intelligence and Sensory Signal Processing 2, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijmissp.2017.088165.

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Luo, Lei, Fayao Liu, Ruizhi Qiao, and Chunhua Shen. "Designing ensemble learning algorithms using kernel methods." International Journal of Machine Intelligence and Sensory Signal Processing 2, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijmissp.2017.10009116.

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HASEGAWA, Hironobu, Toshiyuki NAITO, Mikiharu ARIMURA, and Tohru TAMURA. "MODAL CHOICE ANALYSIS USING ENSEMBLE LEARNING METHODS." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 68, no. 5 (2012): I_773—I_780. http://dx.doi.org/10.2208/jscejipm.68.i_773.

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Verhoeven, Ben, Walter Daelemans, and Tom De Smedt. "Ensemble Methods for Personality Recognition." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 2 (August 3, 2021): 35–38. http://dx.doi.org/10.1609/icwsm.v7i2.14465.

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An important bottleneck in the development of accurate and robust personality recognition systems based on supervised machine learning, is the limited availability of training data, and the high cost involved in collecting it. In this paper, we report on a proof of concept of using ensemble learning as a way to alleviate the data acquisition problem. The approach allows the use of information from datasets from different genres, personality classification systems and even different languages in the construction of a classifier, thereby improving its performance. In the exploratory research described here, we indeed observe the expected positive effects.

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Pahno, Steve, Jidong J. Yang, and S. Sonny Kim. "Use of Machine Learning Algorithms to Predict Subgrade Resilient Modulus." Infrastructures 6, no. 6 (May 21, 2021): 78. http://dx.doi.org/10.3390/infrastructures6060078.

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Modern machine learning methods, such as tree ensembles, have recently become extremely popular due to their versatility and scalability in handling heterogeneous data and have been successfully applied across a wide range of domains. In this study, two widely applied tree ensemble methods, i.e., random forest (parallel ensemble) and gradient boosting (sequential ensemble), were investigated to predict resilient modulus, using routinely collected soil properties. Laboratory test data on sandy soils from nine borrow pits in Georgia were used for model training and testing. For comparison purposes, the two tree ensemble methods were evaluated against a regression tree model and a multiple linear regression model, demonstrating their superior performance. The results revealed that a single tree model generally suffers from high variance, while providing a similar performance to the traditional multiple linear regression model. By leveraging a collection of trees, both tree ensemble methods, Random Forest and eXtreme Gradient Boosting, significantly reduced variance and improved prediction accuracy, with the eXtreme Gradient Boosting being the best model, with an R2 of 0.95 on the test dataset.

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Hozhyi, O. P., O. O. Zhebko, I. O. Kalinina, and T. A. Hannichenko. "Іntelligent classification system based on ensemble methods." System technologies 3, no. 146 (May 11, 2023): 61–75. http://dx.doi.org/10.34185/1562-9945-3-146-2023-07.

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In the paper, based on machine learning methods, the solution of the classification task was investigated using a two-level structure of ensembles of models. To improve forecasting results, an ensemble approach was used: several basic models were trained to solve the same problem, with subsequent aggregation and improvement of the ob-tained results. The problem of classification was studied. The architecture of the intelli-gent classification system is proposed. The system consists of the following components: a subsystem of preprocessing and data analysis, a subsystem of data distribution, a subsystem of building basic models, a subsystem of building and evaluating ensembles of models. A two-level ensemble structure was used to find a compromise between bias and variance inherent in machine learning models. At the first level, an ensemble based on stacking is implemented using a logistic regression model as a metamodel. The pre-dictions that are generated by the underlying models are used as input for training in the first layer. The following basic models of the first layer were chosen: decision trees (DecisionTree), naive Bayesian classifier (NB), quadratic discriminant analysis (QDA), logistic regression (LR), support vector method (SVM), random forest model (RF). The bagging method based on the Bagged CART algorithm was used in the second layer. The algorithm creates N regression trees using M initial training sets and averages the re-sulting predictions. As the basic models of the second layer, the following were chosen: the first-level model (Stacking LR), the model of artificial neural networks (ANN); the linear discriminant analysis (LDA) model and the nearest neighbor (KNN) model. A study of basic classification models and ensemble models based on stacking and bag-ging, as well as metrics for evaluating the effectiveness of the use of basic classifiers and models of the first and second level, was conducted. The following parameters were de-termined for all the methods in the work: prediction accuracy and error rate, Kappa statistic, sensitivity and specificity, accuracy and completeness, F-measure and area under the ROC curve. The advantages and effectiveness of the ensemble of models in comparison with each basic model are determined.

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Kilimci, Zeynep H., and Selim Akyokus. "Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification." Complexity 2018 (October 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7130146.

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The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts.

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Valentini, Giorgio. "Hierarchical Ensemble Methods for Protein Function Prediction." ISRN Bioinformatics 2014 (May 5, 2014): 1–34. http://dx.doi.org/10.1155/2014/901419.

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Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.

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Stevens, Christophe AT, Alexander RM Lyons, Kanika I. Dharmayat, Alireza Mahani, Kausik K. Ray, Antonio J. Vallejo-Vaz, and Mansour TA Sharabiani. "Ensemble machine learning methods in screening electronic health records: A scoping review." DIGITAL HEALTH 9 (January 2023): 205520762311732. http://dx.doi.org/10.1177/20552076231173225.

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Background Electronic health records provide the opportunity to identify undiagnosed individuals likely to have a given disease using machine learning techniques, and who could then benefit from more medical screening and case finding, reducing the number needed to screen with convenience and healthcare cost savings. Ensemble machine learning models combining multiple prediction estimates into one are often said to provide better predictive performances than non-ensemble models. Yet, to our knowledge, no literature review summarises the use and performances of different types of ensemble machine learning models in the context of medical pre-screening. Method We aimed to conduct a scoping review of the literature reporting the derivation of ensemble machine learning models for screening of electronic health records. We searched EMBASE and MEDLINE databases across all years applying a formal search strategy using terms related to medical screening, electronic health records and machine learning. Data were collected, analysed, and reported in accordance with the PRISMA scoping review guideline. Results A total of 3355 articles were retrieved, of which 145 articles met our inclusion criteria and were included in this study. Ensemble machine learning models were increasingly employed across several medical specialties and often outperformed non-ensemble approaches. Ensemble machine learning models with complex combination strategies and heterogeneous classifiers often outperformed other types of ensemble machine learning models but were also less used. Ensemble machine learning models methodologies, processing steps and data sources were often not clearly described. Conclusions Our work highlights the importance of deriving and comparing the performances of different types of ensemble machine learning models when screening electronic health records and underscores the need for more comprehensive reporting of machine learning methodologies employed in clinical research.

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Klaar, Anne Carolina Rodrigues, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico." Energies 16, no. 7 (March 31, 2023): 3184. http://dx.doi.org/10.3390/en16073184.

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The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10−9 in the testing phase.

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Doganer, Adem. "Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods." International Journal of Big Data and Analytics in Healthcare 6, no. 2 (July 2021): 15–30. http://dx.doi.org/10.4018/ijbdah.20210701.oa2.

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In this study, different models were created to reduce bias by ensemble learning methods. Reducing the bias error will improve the classification performance. In order to increase the classification performance, the most appropriate ensemble learning method and ideal sample size were investigated. Bias values and learning performances of different ensemble learning methods were compared. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n: 6000, n: 8000, n: 10000, and n: 20000 sample sizes. When the learning performances were compared, AdaBoost ensemble learning method and RBF classifier achieved the best performance with n: 250 sample size (ACC = 0.956, AUC: 0.987). The AdaBoost ensemble learning method and REPTree classifier achieved the best performance with n: 20000 sample size (ACC = 0.990, AUC = 0.999). In conclusion, for reduction of bias, methods based on stacking displayed a higher performance compared to other methods.

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Grekov, A. N., and A. A. Kabanov. "Ensemble machine learning methods for Euler angles detection in an inertial navigation system." Monitoring systems of environment, no. 1 (March 28, 2022): 112–20. http://dx.doi.org/10.33075/2220-5861-2022-1-112-120.

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The work is focused on increasing the reliability of navigation information of autonomous platforms used in studying oceans and seas, namely: determining the Euler angles using experimental data generated at the output of an inertial navigation system built on the basis of MEMS sensors. Two ensemble methods of machine learning are considered: majority voting (voting by the majority) and weighted majority voting. The ensembles are formed by combining three supervised learning methods: support vector machine (SVM), k-nearest neighbors (KNN), and decision trees. Optimization of hyperparameters of these three classifiers is performed. As a result of combining the optimized classifiers into an ensemble with a weighted majority, an increase in classification accuracy (accuracy) is obtained compared to individual algorithms: 0.92 on the training and test data sets.

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Hart, Emma, and Kevin Sim. "On Constructing Ensembles for Combinatorial Optimisation." Evolutionary Computation 26, no. 1 (March 2018): 67–87. http://dx.doi.org/10.1162/evco_a_00203.

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Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any metaheuristic ensemble, and lead to better understanding of how to undertake principled ensemble design.

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Shibaikin, Sergei, Vladimir Nikulin, and Andrei Abbakumov. "Analysis of machine learning methods for computer systems to ensure safety from fraudulent texts." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2020, no. 1 (January 27, 2020): 29–40. http://dx.doi.org/10.24143/2072-9502-2020-1-29-40.

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IT Security is an essential condition for functioning of each company whose work is related to the information storage. Various models for detecting fraudulent texts including a support vector machine, neural networks, logistic regression, and a naive Bayes classifier, have been analyzed. It is proposed to increase the efficiency of detection of fraudulent messages by combining classifiers in ensembles. The metaclassifier allows to consider the accuracy values of all analyzers, involving in the work the construction of the weight matrix and the characteristic that determines the minimum accuracy boundary. Based on the developed method, a software module for the classification of fraudulent text messages written in Java using M1 class of the OPENCV open library was created and tested. The general algorithm of the ensemble method is given. An experiment based on logistic regression, a naive Bayesian classifier, a multilayer perceptron, and an ensemble of these classifiers has revealed the maximum efficiency of the naive Bayesian classification algorithm and the prospect of combining classifiers into ensembles. The combined methods (ensembles) improve the results and increase the efficiency of the analysis, in contrast to the work of individual analyzers.

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Kim, Kyung-Min, Ha-Young Jang, and Byoung-Tak Zhang. "Oversampling-Based Ensemble Learning Methods for Imbalanced Data." KIISE Transactions on Computing Practices 20, no. 10 (October 15, 2014): 549–54. http://dx.doi.org/10.5626/ktcp.2014.20.10.549.

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Ahmad, Iftikhar, Muhammad Yousaf, Suhail Yousaf, and Muhammad Ovais Ahmad. "Fake News Detection Using Machine Learning Ensemble Methods." Complexity 2020 (October 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/8885861.

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The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

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Hartono, Hartono, Opim Salim Sitompul, Tulus Tulus, Erna Budhiarti Nababan, and Darmawan Napitupulu. "Hybrid Approach Redefinition (HAR) model for optimizing hybrid ensembles in handling class imbalance: a review and research framework." MATEC Web of Conferences 197 (2018): 03003. http://dx.doi.org/10.1051/matecconf/201819703003.

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The purpose of this research is to develop a research framework to optimize the results of hybrid ensembles in handling class imbalance issues. The imbalance class is a state in which the classification results give the number of instances in a class much larger than the number of instances in the other class. In machine learning, this problem can reduce the prediction accuracy and also reduce the quality of the resulting decisions. One of the most popular methods of dealing with class imbalance is the method of ensemble learning. Hybrid Ensembles is an ensemble learning method approach that combines the use of bagging and boosting. Optimization of Hybrid Ensembles is done with the intent to reduce the number of classifier and also obtain better data diversity. Based on an iterative methodology, we review, analyze, and synthesize the current state of the literature and propose a completely new research framework for optimizing Hybrid Ensembles. In doing so, we propose a new taxonomy in ensemble learning that yields a new approach of sampling-based Ensembles and will propose an optimization Hybrid Ensembles using Hybrid Approach Redefinition (HAR) Model that combines the use of Hybrid Ensembles and Sampling Based Ensembles methods. We further provide an empirical analysis of the reviewed literature and emphasize the benefits that can be achieved by optimizing Hybrid Ensembles.

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Anwar, Hina, Usman Qamar, and Abdul Wahab Muzaffar Qureshi. "Global Optimization Ensemble Model for Classification Methods." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/313164.

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Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

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ASSFALG, JOHANNES, JING GONG, HANS-PETER KRIEGEL, ALEXEY PRYAKHIN, TIANDI WEI, and ARTHUR ZIMEK. "SUPERVISED ENSEMBLES OF PREDICTION METHODS FOR SUBCELLULAR LOCALIZATION." Journal of Bioinformatics and Computational Biology 07, no. 02 (April 2009): 269–85. http://dx.doi.org/10.1142/s0219720009004072.

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In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods.

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Ghorpade, Smita Jaywantrao, Ratna Sadashiv Chaudhari, and Seema Sajanrao Patil. "Enhancement of Imbalance Data Classification with Boosting Methods: An Experiment." ECS Transactions 107, no. 1 (April 24, 2022): 15923–34. http://dx.doi.org/10.1149/10701.15923ecst.

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The idea of boosting emanates from the area of machine learning. It is a challenging task for imbalance data set to have appropriate distribution of data samples in each class by machine learning algorithm. To deal with this problem, ensemble learning method is one of the popular approaches. Ensemble methods integrate several learning algorithms, which gives better predictive performance as compared to any of the basic learning algorithms alone. Based on this research, a question is formulated. The null hypothesis is stated as “There is no significant difference between single classifier and classifier with ensemble techniques - AdaboostM1 and Bagging.” Alternative hypothesis is stated as “Ensemble techniques AdaBoostM1 and Bagging works more superior as compare to single classifier.” We have conducted an experiment on three imbalanced data sets. We examined the accuracy of four classifiers Naïve Bayes, Multilayer Perceptron, Locally Weighted Learning, and REPTree. The predicted accuracy score of these classifiers are compared with boosting techniques AdaboostM1, Bagging, Voting, and Stacking.

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Siqueira, Henrique, Sven Magg, and Stefan Wermter. "Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5800–5809. http://dx.doi.org/10.1609/aaai.v34i04.6037.

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Ensemble methods, traditionally built with independently trained de-correlated models, have proven to be efficient methods for reducing the remaining residual generalization error, which results in robust and accurate methods for real-world applications. In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient. In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions. We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR without loss of diversity and generalization power, which are both important for ensemble performance. Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.

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Osareh, Alireza, and Bita Shadgar. "An Efficient Ensemble Learning Method for Gene Microarray Classification." BioMed Research International 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/478410.

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The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

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WINDEATT, T., and G. ARDESHIR. "DECISION TREE SIMPLIFICATION FOR CLASSIFIER ENSEMBLES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (August 2004): 749–76. http://dx.doi.org/10.1142/s021800140400340x.

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The goal of designing an ensemble of simple classifiers is to improve the accuracy of a recognition system. However, the performance of ensemble methods is problem-dependent and the classifier learning algorithm has an important influence on ensemble performance. In particular, base classifiers that are too complex may result in overfitting. In this paper, the performance of Bagging, Boosting and Error-Correcting Output Code (ECOC) is compared for five decision tree pruning methods. A description is given for each of the pruning methods and the ensemble techniques. AdaBoost.OC which is a combination of Boosting and ECOC is compared with the pseudo-loss based version of Boosting, AdaBoost.M2 and the influence of pruning on the performance of the ensembles is studied. Motivated by the result that both pruned and unpruned ensembles made by AdaBoost.OC give similar accuracy, pruned ensembles are compared with ensembles of Decision Stumps. This leads to the hypothesis that ensembles of simple classifiers may give better performance for some problems. Using the application of face recognition, it is shown that an AdaBoost.OC ensemble of Decision Stumps outperforms an ensemble of pruned C4.5 trees for face identification, but is inferior for face verification. The implication is that in some real-world tasks to achieve best accuracy of an ensemble, it may be necessary to select base classifier complexity.

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Cai, Cheng, Ahmad P. Tafti, Che Ngufor, Pei Zhang, Peilin Xiao, Mingyan Dai, Hongfang Liu, et al. "Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization." Journal of Cardiovascular Electrophysiology 32, no. 9 (July 27, 2021): 2504–14. http://dx.doi.org/10.1111/jce.15171.

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Evdokimova, Kseniya V., and Kirill L. Tassov. "ANALYSIS OF ALGORITHMS BUILT ON “WEAK EXPERTS”." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 2 (2022): 33–47. http://dx.doi.org/10.28995/2686-679x-2022-2-33-47.

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Machine learning is a rather complex process, since its implementation requires optimization of the training dataset, time and quality of training. In some cases, that optimization is difficult to achieve, and ensemble learning is often used. Ensemble learning can generate predictions from multiple sources or classifiers based on the reliability and experience of each source. As a result, the task of such learning is to build a system of classifiers in order to obtain an accurate forecast. The article introduces the ensembles of experts in machine learning and considers the development, introduction and application of such algorithms, in particular, examines the conditions under which systems based on ensembles can be more effective than their counterparts based on a single classifier. A classification of algorithms based on “weak experts” is given, and the methods used by the main ones are described. The article also specifies the advantages and disadvantages of each type of “weak expert” ensembles. A comparative table of ensembles data was built, taking into account the required amount of training sample, the type of experts used in the ensemble and the percentage of recognition

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Kapil, Divya. "Enhancing MNIST Digit Recognition with Ensemble Learning Techniques." Mathematical Statistician and Engineering Applications 70, no. 2 (February 26, 2021): 1362–71. http://dx.doi.org/10.17762/msea.v70i2.2328.

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Abstract The classification task known as MNIST digit recognition involves identifying handwritten numbers into their corresponding values. Although there are numerous approaches proposed for this type of task, they typically face issues in achieving high accuracy. One method that can improve single models' performance is through ensemble learning. The goal of this study is to explore the use of various learning techniques, such as boosting and bagging, in combination with random forest models and decision trees, to improve the performance of MNIST digit recognition with regard to accuracy. We then perform evaluations on these methods using various metrics, such as recall, precision, accuracy, and F1. The findings of this study provide valuable insight into the various advantages of ensemble methods for the MNIST digit recognition task. It also highlights the need to explore these techniques in the context of machine learning. The objective of this study is to investigate the use of ensembles in improving the accuracy of MNIST digit recognition. We performed evaluations on two popular methods, namely boosting and bagging, with random forest and decision tree models. The evaluation parameters included F1 score, recall, accuracy, and precision. The results of the evaluations revealed that both boosting and bagging methods performed well in terms of their evaluation metrics. In most cases, the decision tree performed better than the random forest. However, the random forest method was able to achieve the highest accuracy, which is 99 percent. The findings of the evaluation revealed that ensembles can help improve single models' accuracy in MNIST digit recognition. On the other hand, the random forest method is a promising option for this task. The exact results of the evaluations will vary depending on the evaluation and implementation metrics. More research is needed to confirm their generalizability. The study emphasizes the value of exploring ensembles in machine learning systems, as well as the potential advantages of performing MNIST digit recognition using them.

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Rajaraman, Sivaramakrishnan, Feng Yang, Ghada Zamzmi, Zhiyun Xue, and Sameer K. Antani. "A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs." Bioengineering 9, no. 9 (August 24, 2022): 413. http://dx.doi.org/10.3390/bioengineering9090413.

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Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical decision-making, and potentially result in improved patient treatment. The majority of works in the literature discuss training automatic segmentation models using coarse bounding box annotations. However, the granularity of the bounding box annotation could result in the inclusion of a considerable fraction of false positives and negatives at the pixel level that may adversely impact overall semantic segmentation performance. This study evaluates the benefits of using fine-grained annotations of TB-consistent lesions toward training the variants of U-Net models and constructing their ensembles for semantically segmenting TB-consistent lesions in both original and bone-suppressed frontal CXRs. The segmentation performance is evaluated using several ensemble methods such as bitwise- AND, bitwise-OR, bitwise-MAX, and stacking. Extensive empirical evaluations showcased that the stacking ensemble demonstrated superior segmentation performance (Dice score: 0.5743, 95% confidence interval: (0.4055, 0.7431)) compared to the individual constituent models and other ensemble methods. To the best of our knowledge, this is the first study to apply ensemble learning to improve fine-grained TB-consistent lesion segmentation performance.

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Li, Ziyue, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang, and Dongsheng Li. "Towards Inference Efficient Deep Ensemble Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8711–19. http://dx.doi.org/10.1609/aaai.v37i7.26048.

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Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However, we found that the majority of computations in ensemble methods are redundant. For instance, over 77% of samples in CIFAR-100 dataset can be correctly classified with only a single ResNet-18 model, which indicates that only around 23% of the samples need an ensemble of extra models. To this end, we propose an inference efficient ensemble learning method, to simultaneously optimize for effectiveness and efficiency in ensemble learning. More specifically, we regard ensemble of models as a sequential inference process and learn the optimal halting event for inference on a specific sample. At each timestep of the inference process, a common selector judges if the current ensemble has reached ensemble effectiveness and halt further inference, otherwise filters this challenging sample for the subsequent models to conduct more powerful ensemble. Both the base models and common selector are jointly optimized to dynamically adjust ensemble inference for different samples with various hardness, through the novel optimization goals including sequential ensemble boosting and computation saving. The experiments with different backbones on real-world datasets illustrate our method can bring up to 56% inference cost reduction while maintaining comparable performance to full ensemble, achieving significantly better ensemble utility than other baselines. Code and supplemental materials are available at https://seqml.github.io/irene.

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Alharbi, Amal, Manal Kalkatawi, and Mounira Taileb. "Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods." Arabian Journal for Science and Engineering 46, no. 9 (May 20, 2021): 8913–23. http://dx.doi.org/10.1007/s13369-021-05475-0.

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ADEBOYE, OLATUNBOSUN, and KESTER AWANI. "ENSEMBLE LEARNING METHODS FOR PREDICTING ROP IN DIRECTIONAL WELLS." i-manager’s Journal on Instrumentation and Control Engineering 7, no. 3 (2019): 7. http://dx.doi.org/10.26634/jic.7.3.17187.

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Bekdaş, Gebrail, Celal Cakiroglu, Kamrul Islam, Sanghun Kim, and Zong Woo Geem. "Optimum Design of Cylindrical Walls Using Ensemble Learning Methods." Applied Sciences 12, no. 4 (February 18, 2022): 2165. http://dx.doi.org/10.3390/app12042165.

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The optimum cost of the structure design is one of the major goals of structural engineers. The availability of large datasets with preoptimized structural configurations can facilitate the process of optimum design significantly. The current study uses a dataset of 7744 optimum design configurations for a cylindrical water tank. Each of them was obtained by using the harmony search algorithm. The database used contains unique combinations of height, radius, total cost, material unit cost, and corresponding wall thickness that minimize the total cost. It was used to create ensemble learning models such as Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Gradient Boosting (CatBoost). Generated machine learning models were able to predict the optimum wall thickness corresponding to new data with high accuracy. Using SHapely Additive exPlanations (SHAP), the height of a cylindrical wall was found to have the greatest impact on the optimum wall thickness followed by radius and the ratio of concrete unit cost to steel unit cost.

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Prayogo, D., D. I. Santoso, D. Wijaya, T. Gunawan, and J. A. Widjaja. "Prediction of Concrete Properties Using Ensemble Machine Learning Methods." Journal of Physics: Conference Series 1625 (September 2020): 012024. http://dx.doi.org/10.1088/1742-6596/1625/1/012024.

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Wang, Shuo, Leandro L. Minku, and Xin Yao. "Resampling-Based Ensemble Methods for Online Class Imbalance Learning." IEEE Transactions on Knowledge and Data Engineering 27, no. 5 (May 1, 2015): 1356–68. http://dx.doi.org/10.1109/tkde.2014.2345380.

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King, Michael A., Alan S. Abrahams, and Cliff T. Ragsdale. "Ensemble learning methods for pay-per-click campaign management." Expert Systems with Applications 42, no. 10 (June 2015): 4818–29. http://dx.doi.org/10.1016/j.eswa.2015.01.047.

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Liang, Weizhang, Asli Sari, Guoyan Zhao, Stephen D. McKinnon, and Hao Wu. "Short-term rockburst risk prediction using ensemble learning methods." Natural Hazards 104, no. 2 (August 28, 2020): 1923–46. http://dx.doi.org/10.1007/s11069-020-04255-7.

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Ayon, Roy D. Gregori, Md Sanaullah Rabbi, Umme Habiba, and Maoyejatun Hasana. "Bangla Speech Emotion Detection using Machine Learning Ensemble Methods." Advances in Science, Technology and Engineering Systems Journal 7, no. 6 (November 2022): 70–76. http://dx.doi.org/10.25046/aj070608.

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Whitaker, Tim, and Darrell Whitley. "Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8638–46. http://dx.doi.org/10.1609/aaai.v36i8.20842.

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Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associated with training several independent networks becomes expensive. We introduce a fast, low-cost method for creating diverse ensembles of neural networks without needing to train multiple models from scratch. We do this by first training a single parent network. We then create child networks by cloning the parent and dramatically pruning the parameters of each child to create an ensemble of members with unique and diverse topologies. We then briefly train each child network for a small number of epochs, which now converge significantly faster when compared to training from scratch. We explore various ways to maximize diversity in the child networks, including the use of anti-random pruning and one-cycle tuning. This diversity enables "Prune and Tune" ensembles to achieve results that are competitive with traditional ensembles at a fraction of the training cost. We benchmark our approach against state of the art low-cost ensemble methods and display marked improvement in both accuracy and uncertainty estimation on CIFAR-10 and CIFAR-100.

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Klaiber, Marco. "A Fundamental Overview of SOTA-Ensemble Learning Methods for Deep Learning: A Systematic Literature Review." Science in Information Technology Letters 2, no. 2 (December 30, 2021): 1–14. http://dx.doi.org/10.31763/sitech.v2i2.549.

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The rapid growth in popularity of Deep Learning (DL) continues to bring more use cases and opportunities, with methods rapidly evolving and new fields developing from the convergence of different algorithms. For this systematic literature review, we considered the most relevant peer-reviewed journals and conference papers on the state of the art of various Ensemble Learning (EL) methods for application in DL, which are also expected to give rise to new ones in combination. The EL methods relevant to this work are described in detail and the respective popular combination strategies as well as the individual tuning and averaging procedures are presented. A comprehensive overview of the various limitations of EL is then provided, culminating in the final formulation of research gaps for future scholarly work on the results, which is the goal of this thesis. This work fills the research gap for upcoming work in EL for by proving in detail and making accessible the fundamental properties of the chosen methods, which will further deepen the understanding of the complex topic in the future and, following the maxim of ensemble learning, should enable better results through an ensemble of knowledge in the future.

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You, Weizhen, Alexandre Saidi, Abdel-malek Zine, and Mohamed Ichchou. "Mechanical Reliability Assessment by Ensemble Learning." Vehicles 2, no. 1 (February 14, 2020): 126–41. http://dx.doi.org/10.3390/vehicles2010007.

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Reliability assessment plays a significant role in mechanical design and improvement processes. Uncertainties in structural properties as well as those in the stochatic excitations have made reliability analysis more difficult to apply. In fact, reliability evaluations involve estimations of the so-called conditional failure probability (CFP) that can be seen as a regression problem taking the structural uncertainties as input and the CFPs as output. As powerful ensemble learning methods in a machine learning (ML) domain, random forest (RF), and its variants Gradient boosting (GB), Extra-trees (ETs) always show good performance in handling non-parametric regressions. However, no systematic studies of such methods in mechanical reliability are found in the current published research. Another more complex ensemble method, i.e., Stacking (Stacked Generalization), tries to build the regression model hierarchically, resulting in a meta-learner induced from various base learners. This research aims to build a framework that integrates ensemble learning theories in mechanical reliability estimations and explore their performances on different complexities of structures. In numerical simulations, the proposed methods are tested based on different ensemble models and their performances are compared and analyzed from different perspectives. The simulation results show that, with much less analysis of structural samples, the ensemble learning methods achieve highly comparable estimations with those by direct Monte Carlo simulation (MCS).

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Ren, Lulu, Feixiang Li, Bairu Chen, Qian Chen, Guanqiong Ye, and Xuchao Yang. "China’s Wealth Capital Stock Mapping via Machine Learning Methods." Remote Sensing 15, no. 3 (January 24, 2023): 689. http://dx.doi.org/10.3390/rs15030689.

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The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R2 = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data.

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Zhou, Peng, Liang Du, Yi-Dong Shen, and Xuejun Li. "Tri-level Robust Clustering Ensemble with Multiple Graph Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11125–33. http://dx.doi.org/10.1609/aaai.v35i12.17327.

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Clustering ensemble generates a consensus clustering result by integrating multiple weak base clustering results. Although it often provides more robust results compared with single clustering methods, it still suffers from the robustness problem if it does not treat the unreliability of base results carefully. Conventional clustering ensemble methods often use all data for ensemble, while ignoring the noises or outliers on the data. Although some robust clustering ensemble methods are proposed, which extract the noises on the data, they still characterize the robustness in a single level, and thus they cannot comprehensively handle the complicated robustness problem. In this paper, to address this problem, we propose a novel Tri-level Robust Clustering Ensemble (TRCE) method by transforming the clustering ensemble problem to a multiple graph learning problem. Just as its name implies, the proposed method tackles robustness problem in three levels: base clustering level, graph level and instance level. By considering the robustness problem in a more comprehensive way, the proposed TRCE can achieve a more robust consensus clustering result. Experimental results on benchmark datasets also demonstrate it. Our method often outperforms other state-of-the-art clustering ensemble methods. Even compared with the robust ensemble methods, ours also performs better.

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Yaman, Emine, and Abdulhamit Subasi. "Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification." BioMed Research International 2019 (October 31, 2019): 1–13. http://dx.doi.org/10.1155/2019/9152506.

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The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers’ efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

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Hapsari, Dian Puspita, Waras Lumandi, and Arief Rachman. "HOSPITAL LENGTH OF STAY PREDICTION WITH ENSEMBLE LEARNING METHODE." Journal of Applied Sciences, Management and Engineering Technology 4, no. 1 (June 5, 2023): 29–36. http://dx.doi.org/10.31284/j.jasmet.2023.v4i1.4437.

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The hospital length of stay (LoS) is the number of days an inpatient will stay in the hospital. LoS is used as a measure of hospital performance so they can improve the quality of service to patients better. However, making an accurate estimate of LoS can be difficult due to the many factors that influence it. The research conducted aims to predict LoS for treated patients (ICU and non-ICU) with cases of brain vessel injuries by using the ensemble learning method. The Random Forest algorithm is one of the ensembles learning methods used to predict LoS in this study. The dataset used in this study is primary data at PHC Surabaya Hospital. From the results of the simulations performed, the random forest algorithm is able to predict LoS in a dataset of treated patients (ICU and non-ICU) with cases of brain vessel injuries. And the simulation results show a type II error value of 0.10 while the value of type I error is 0.16.

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Journal articles on the topic 'Ensemble learning methods'

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