Tae-Hwy Lee, Jianghao Chu, Aman Ullah et al. · RePEc: Research Papers in Economics
In the era of Big Data, selecting relevant variables from a potentially large pool of candidate variables becomes a newly emerged concern in macroeconomic researches, especially when the data available is high-dimensional, i.e. the number of explanatory variables (p) is greater than the length of the sample size (n). Common approaches include factor models, the principal component analysis and regularized regressions. However, these methods require additional assumptions that are hard to verify and/or introduce biases or aggregated factors which complicate the interpretation of the estimated outputs. This chapter reviews an alternative solution, namely Boosting, which is able to estimate the variables of interest consistently under fairly general conditions given a large set of explanatory variables. Boosting is fast and easy to implement which makes it one of the most popular machine learning algorithms in academia and industry.
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Sanjeev Kulkarni, Gilbert Harman · Wiley series in probability and statistics · 2011
Boosting combines a set of “weak” classification rules to produce a “strong” composite classifier. There are a number of variants of the basic boosting algorithm, one of the most popular of which is the AdaBoost algorithm developed by Freund and Schapire. This chapter provides some background on weak learners and two of the key ingredients that are needed, namely, the ideas of combining classification rules and of updating the weightings of the training examples. Boosting works through a series of rounds, and in each round a weak classifier is produced that focuses more attention on those training examples that have been misclassified in the previous round. The final classifier is then formed as a weighted combination of the weak classifiers produced in each round, where the weights are based on the performance of the weak classifiers.
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Wolfgang Karl Härdle, Xiaorui Zuo · 2021
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University of Pennsylvania Press, Inc. eBooks · 2024
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Graham R. Williams · 2011
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Concha Bielza, Pedro Larrañaga · Dictionary of Bioinformatics and Computational Biology · 2004
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Encyclopedia of Machine Learning · 2010
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David Forsyth · 2019
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Hang Li · 2023
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Matthias Schonlau · Statisctics and computing/Statistics and computing · 2023
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C. Y. Wang, Ziding Feng · 2009
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Tae-Hwy Lee, Jianghao Chu, Aman Ullah et al. · RePEc: Research Papers in Economics
In the era of Big Data, selecting relevant variables from a potentially large pool of candidate variables becomes a newly emerged concern in macroeconomic researches, especially when the data available is high-dimensional, i.e. the number of explanatory variables (p) is greater than the length of the sample size (n). Common approaches include factor models, the principal component analysis and regularized regressions. However, these methods require additional assumptions that are hard to verify and/or introduce biases or aggregated factors which complicate the interpretation of the estimated outputs. This chapter reviews an alternative solution, namely Boosting, which is able to estimate the variables of interest consistently under fairly general conditions given a large set of explanatory variables. Boosting is fast and easy to implement which makes it one of the most popular machine learning algorithms in academia and industry.
Cited by 0
Mutlu Yuksel, Yigit Aydede · 2025
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Zhi‐Hua Zhou · 2025
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Sandeep Singh, Guy W. Fried · Medicine & Science in Sports & Exercise · 2006
Many athletes with spinal cord injury voluntarily induce autonomic dysreflexia (AD) to enhance performance. This is better known as “boosting”. This technique is usually employed before or during a competition and is applied primarily to overcome the physiologic and metabolic limitations that the spinal cord injured athlete may encounter. Although the performance is enhanced, there can be potential life-threatening consequences. These consequences include hemorrhagic stroke, cardiac arrhythmia, myocardial infarction and death. PURPOSE: To illustrate how boosting significantly improves or enhances performance outcome. METHODS: In this study, a spinal cord injured athlete was observed performing boosting. Measures of performance time and cardiovascular response were recorded with and without boosting. His baseline performance status was compared to the performance status while boosting. The athlete was observed performing wheelchair sprinting for a distance of 0.13 miles. The variables recorded before and after the activity included blood pressure, heart rate, pulse oximetry, respiration rate, and performance time. RESULTS: The results collected illustrated a 30 mm Hg increase in systolic and diastolic blood pressure during the induction period of AD when compared to baseline values. Elevation of heart rate by 15 beats per minute was also noted. The respiratory rate increased by 4 breaths per minute, along with a 1% decrease in oxygen saturation. Comparison of the “boosted” task to the “unboosted” task reveals disparity. The diastolic blood pressure was elevated by 35 mm Hg, and there was a 20 mm Hg increase in systolic blood pressure for the “boosted” task. Heart rate, respiratory rate, and oxygen saturation were relatively unchanged. Performance time showed a decrease of about 5.3 seconds (39.21 with AD, and 44.53 without AD). Table: Vital signs recorded at different intervals of performances BP (mm Hg) Hr (bpm) rr (breaths/min) PO2 (%) Pt (sec) Voluntary induction of AD provides a performance advantage. Given the data, an improvement in performance time by 5.32 seconds over a distance of 0.13 miles (200 meters) existed. Performance enhancement is calculated to be 13.56%. CONCLUSION: It is apparent that a significant performance improvement exists in the athlete who applies the boosting technique. Although boosting can create a performance advantage, it can cause a significant risk of mortality and morbidity for the SCI athlete. This practice should be discouraged by all physicians and health care providers.
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2012
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Robert E. Schapire, Yoav Freund · The MIT Press eBooks · 2012
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
Cited by 397Open access
Thijs Becker, Melvin Geubbelmans, Axel-Jan Rousseau et al. · American Journal of Orthodontics and Dentofacial Orthopedics · 2023
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Yann Ricquebourg, Christian Raymond, Baptiste Poirriez et al. · 2013
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Satria Mandala (8843060), Ardian Rizal (15372916), Adiwijaya (8843069) et al. · Figshare · 2024
<div><p>Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.</p></div>
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