Ensemble Learning

Langford, John; Zhang, Xinhua; Brown, Gavin; Bhattacharya, Indrajit; Getoor, Lise; Zeugmann, Thomas; Todorovski, Ljupčo; Ting, Kai Ming; Corne, David; Handl, Julia; Knowles, Joshua; García-Almanza, AlmaLilia; Centeno, Tonatiuh Peña; García-Almanza, Alma Lilia; Alexandrova-Kabadjova, Biliana; Martínez-Jaramillo, Serafín; Krawiec, Krzysztof; Kavka, Carlos; Sipper, Moshe; Igel, Christian; Husbands, Phil; Jin, Xin; Heskes, Tom; DeJong, Gerald; Lim, Shiau Hong; Kambhampati, Subbarao; Yoon, Sungwook

doi:10.1007/978-0-387-30164-8_252

Cited by 53 publications

(6 citation statements)

References 16 publications

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“…The classifiers are subsequently aggregated using a majority voting mechanism, where the ensemble decision is determined by the class selected by the majority of classifiers for a given case. [25]. The underlying theory is that subsequent iterations should compensate for mistakes made by previous models, leading to an overall improvement in the ensemble's performance.…”

Section: B Ensemble Learningmentioning

confidence: 99%

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Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IEEE Access

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Software Bug Prediction (SBP) is an integral process to the software's success that involves predicting software bugs before their occurrence. Detecting software bugs early in the development process enhances software quality, performance, and reduces software costs. The integration of Machine Learning (ML) algorithms has significantly improved software bug prediction accuracy and concurrently reduced costs and resource utilization. Numerous studies have explored the impact of Hyperparameter Optimization on single classifiers, enhancing these models' overall performance in SBP analysis. Ensemble Learning (EL) approaches have also demonstrated increased model accuracy and performance on SBP datasets. This study proposes a novel learning model for predicting software bugs through the utilization of EL and tuning hyperparameters. The results are compared with single hypothesis learning models using the WEKA software. The dataset, collected by the National Aeronautics and Space Administration (NASA) U.S.A., comprises 10,885 instances with 20 attributes, including a classifier for defects in one of their coding projects. The findings indicate that EL models outperform single hypothesis learning models, and the proposed model's accuracy increases after optimization. Furthermore, the accuracy of the proposed model demonstrates improvement following the optimization process. These results underscore the efficacy of ensemble learning, coupled with hyperparameter optimization, as a viable approach for enhancing the predictive capabilities of software bug prediction models.

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Section: B Ensemble Learningmentioning

confidence: 99%

“…Bagging is particularly effective for unpredictable models that exhibit varying generalization behavior with slight changes in the training data [25]. These models, often termed high variance models, encompass examples like Decision Trees and Neural Networks.…”

Section: B Ensemble Learningmentioning

confidence: 99%

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IEEE Access

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“…Among the most widely used models are random forests, decision trees, and logistic regression; these were selected because of their propensity to handle continuous and categorical data and to represent intricate interactions between variables (White & Jones, 2022). For example, Brown and Nguyen's (2023) analysis of past rocket launches showed that ensemble techniques, such as random forests, might considerably outperform single-predictor models in terms of launch outcome prediction.…”

Section: Machine Learning Models For Rocket Launch Predictionmentioning

confidence: 99%

Mastering the Cosmos: Leveraging Machine Learning to Optimize Spacex Falcon 9 Launch Success Rates

Edafetanure-Ibeh

2024

Preprint

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This study explores the predictive analysis of SpaceX Falcon 9 rocket launch outcomes, utilizing multiple machine learning models to improve prediction success rates. Rocket launch efficiency and dependability have become critical as the commercial space exploration industry proliferates (brown et al., 2023). This exploration period was led by the Falcon 9 rocket, renowned for its revolutionary reusability feature. Predicting results, however, is extremely difficult due to the intricacy of launch variables. This study examines historical launch data through extensive data collecting and organization, concentrating on elements like cargo weight, launch site conditions, and rocket parameters. The research attempts to determine the most accurate predictor of launch success by utilizing many classification models, such as logistic regression, decision trees, and random forests. By identifying significant trends and variables that have a major impact on launch outcomes, the results provide valuable information for improving mission planning and lowering operating risks. This research and its contribution to the corpus of knowledge in predictive analytics and aerospace engineering affect the future of sustainable and affordable space exploration. The results represent a significant advancement in the pursuit of efficient space exploration as they highlight the potential of machine learning to improve the predictability and reliability of commercial space missions.

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“…The Ensemble model is created by learning several individual models and synthesizing the results (Brown, 2010). By generalizing the model through the process of synthesizing the result values, the expected value of the prediction error is smaller than that of a single model, and the predictive accuracy is higher (Polikar, 2006;Zhou, 2012).…”

Section: Ensemblementioning

confidence: 99%

Comparative analysis of model performance for predicting the customer of cafeteria using unstructured data

Kim,

Gu,

Moon

et al. 2023

Communications for Statistical Applications and Methods

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This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

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Ensemble Learning

Cited by 53 publications

References 16 publications

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Mastering the Cosmos: Leveraging Machine Learning to Optimize Spacex Falcon 9 Launch Success Rates

Comparative analysis of model performance for predicting the customer of cafeteria using unstructured data

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