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AdaBoost

Encyclopedia of Biometrics · 2009

Cited by 4

AdaBoost

Lodi, Stefano · AMS Campus - AlmaDL · 2017

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Adaboost

Liu Bo-qi (21519354), Zhou Ding-jie (21519357), Zhao Yang (358244) et al. · Figshare · 2025

、BP-NN、SVR、XGBoost and GB Comparison of model actual values and test values.

Cited by 0Open access

AdaBoost

Paolo Favaro, Andrea Vedaldi · 2021

Cited by 4

Adaboost

Liu Bo-qi (21519354), Zhou Ding-jie (21519357), Zhao Yang (358244) et al. · Figshare · 2025

<div> Effluent quality prediction is critical for optimizing Wastewater Treatment Plant (WWTP) operations, ensuring regulatory compliance, and promoting environmental sustainability. This study evaluates the performance of five supervised learning models—AdaBoost, Backpropagation Neural Networks (BP-NN), Support Vector Machine (SVR), XGBoost, and Gradient Boosting (GB)—using data from a WWTP in Zhuhai, China. The Effluent Quality Index (EQI), integrating multiple pollutant concentrations and environmental impacts, was used as the target variable. The models were trained and tested on 84 monthly datasets, with their performances compared using R², Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE). XGBoost achieved the best balance between accuracy and robustness, with the lowest MAPE(6.11%) and a high R²(0.813), while SVR excelled in fitting accuracy(R² = 0.826) but showed limitations in error control. Although we employed GridSearchCV with cross-validation to optimize hyperparameters and ensure a fair model comparison, the study is limited by the reliance on data from a single WWTP and the relatively small dataset size (84 records). Nevertheless, the findings provide valuable insights into selecting effective machine learning models for effluent quality prediction, supporting data-driven decision-making in wastewater management and advancing intelligent process optimization in WWTP. </div>

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Adaboost

Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning · 2010

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AdaBoost

Helmut Grabner · 2008

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??????????????????(Adaboost)??? ????????????????????? ????????????????????? ?????? ??????

????????? · The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry) · 2010

?????? ???????????? ???????????? ??????????????? ????????? ????????? ???????????? Adaboost??? ???????????? ??????. Adaboost??? ????????? ??????????????? ???????????? ????????? ????????? ????????? ????????????, ????????? ???????????? ????????? ???????????? ??????. ?????????, Adaboost??? ?????? ?????????????????? ???????????? ?????? ????????? ????????? ????????? ??? ??????. ??????????????? ?????? ?????? ????????? ???????????? ???????????? One-Vs-All ????????? Pair-Wise ????????? ???????????????. ????????? ??? ????????? ?????? ?????? ????????? ?????? ?????? ?????? ?????? ????????? ????????????, ????????? ?????? ???????????? ?????? ????????? ????????? ????????????????????? ???????????? ???????????? ?????? ????????? ????????? ??????????????? ?????? ????????? ???????????? ????????? ????????? ???????????????. ??? ??????????????? ?????? ???????????? Adaboost??? ?????? ?????? ?????? ???????????? ?????? ?????? ????????? ?????? ???????????? ???????????? Adaboost ?????? ???????????? ?????? ????????? ????????????.

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Explaining AdaBoost

Robert E. Schapire · 2013

Cited by 950

Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better

2008

Boosting is a technique of combining a set weak classifiers to form one high-performance prediction rule. Boosting was successfully applied to solve the problems of object detection, text analysis, data mining and etc. The most and widely used boosting algorithm is AdaBoost and its later more effective variations Gentle and Real AdaBoost. In this article we propose a new boosting algorithm, which produces less generalization error compared to mentioned algorithms at the cost of somewhat higher training error.

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Application of AdaBoost

Wrhel, Vladimír · Brno University of Technology Digital Library (Brno University of Technology) · 2008

Basics of classification and pattern recognitions will be mentioned in this work. We will focus mainly on AdaBoost algorithm, which serves to create a strong classifier function by some weak classifiers. We shall get acquainted with some modifications of AdaBoost. These modifications improve some of AdaBoost attributes. We shall also look into weak classifiers and features applicable to them. We shall especially look into the Haar- likes features. We shall discus possibilities of using the mentioned algorithms and features in facial expression recognition. We shall describe the situation between facial expression databases. We shall draw out a possible implementation of application of facial expression recognition.

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AdaBoost Confusion Matrices.

Lirika Sola (21560710), Youdinghuan Chen (6067190), V. S. Subrahmanian (14100316) · Figshare · 2025

<div> We provide a novel quantitative geospatial analysis of school attacks perpetrated by Boko Haram in Nigeria. Such attacks are used by Boko Haram to kidnap boys (for potential use as child soldiers and suicide bombers) and girls (for potential use as domestic servants, as sex slaves, and suicide bombers). We first build a novel geospatially tagged data set spanning almost 15 years (July 2009 to April 2023) of data not only on Boko Haram attacks on schools (our dependent variable) but also a set of 15 independent variables (or features) about other attacks by Boko Haram, locations of security installations, as well as socioeconomic and geospatial characteristics of the regions around these schools. Second, we develop a univariate statistical analysis of this data, showing strong links between three broad factors affecting attacks on schools: <i>Security</i> presence in and around a school, the Boko Haram <i>Activity</i> in the area around a school, and the <i>Socioeconomic</i> characteristics of the region around a school. Third, we train several predictive machine learning models and assess their predictive efficacy. The results show that some of these models can accurately quantify the likelihood that a school will be at risk of a Boko Haram attack. In addition, they cast light on the features that are most important in making such predictions. We then analyze learned decision trees to identify some conditions on the independent variables that help predict Boko Haram attacks on school. Fourth, we use these decision trees to formulate multivariate hypotheses that we investigate further from a statistical perspective. We find that Security presence near schools, Activity of Boko Haram in regions, and the Socioeconomic factors characterizing the region a school is in are all significant predictors of attacks. We conclude with a policy recommendation. </div>

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The AdaBoost algorithm

Kuang-Yu Liu, Lin, Jennifer, Xiaobo Zhou et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "Boosting alternating decision trees modeling of disease trait information"BMC Genetics 2005;6(Suppl 1):S132-S132.Published online 30 Dec 2005PMCID:PMC1866804.

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Boosting and AdaBoost

IGI Global eBooks · 2011

Cited by 0

Adaboost classifier results.

Balraj Preet Kaur (20370832), Harpreet Singh (677440), Rahul Hans (20370835) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. Moreover, to enhance the efficacy of the predictions made by hyperparameter-optimized machine learning models, an ensemble model is proposed using a stacking classifier. Also, explainable AI was incorporated to define the feature importance by making use of Shapely adaptive explanations (SHAP) values. For the experimentation, the publicly accessible Mexico clinical dataset of COVID-19 was used. The results obtained show that the proposed model has superior prediction accuracy in comparison to its counterparts. Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. The various performance assessment metrics, including accuracy, precision, recall, AUC, and F1-score, were used to assess the results.&lt;/p&gt;&lt;/div&gt;

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AdaBoost training flowchart.

Yang Guo (399435), Zengrui Tian (21175730), Hong Wang (13528) et al. · Figshare · 2025

<div> The early minor faults generated by the chiller in operation are not easy to perceive, and the severity will gradually increase with time. The traditional fault diagnosis method has low accuracy and poor stability for early fault diagnosis. In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. HINGO enhances the uniformity of the initial population distribution by means of refraction opposition-based learning strategy in initialization, and improves the local and global search ability of the algorithm by means of sine and cosine strategy, Lévy flight and nonlinear decreasing factor in the search stage. The HINGO-LSSVM-IAdaBoost model is trained and validated on the typical air conditioning fault samples of ASHRAE RP-1043. Compared with the traditional methods, the HINGO-LSSVM-IAdaBoost model shows obvious advantages for the early fault diagnosis of chiller units. </div>

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Hierarchical MultiClass AdaBoost

Charalampos Chelmis, Wenting Qi · 2021 IEEE International Conference on Big Data (Big Data) · 2021

One of the most challenging machine learning problems is a particular case of classification in which classes are hierarchically structured and data instances can be assigned multiple labels residing in a path of the hierarchy. In this paper, we propose hierarchy–aware multiclass AdaBoost, allowing for the first time weak classifiers in an ensemble learning setting to be trained for hierarchical multiclass classification while incorporating a hierarchy–aware loss function directly into the training process. Experimental results on numerous real–world datasets show that, despite its simplicity, the proposed algorithm outperforms all baselines, arising as the state of the art in hierarchical multiclass classification.

Cited by 6

Asymmetric Real Adaboost

Zhanjun Wang, Chi Fang, Xiaoqing Ding · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008

A cost-sensitive extension of Real Adaboost denoted as asymmetric Real Adaboost(RAB) is proposed. The two main differences between Asymmetric RAB and the naïve RAB are (1) a Chernoff measurement is used to evaluate the best weak classifier during training, rather than a Bhattacharyya measurement used in naïve RAB, and (2) the weights are updated separately for positives and negatives at each boosting step. The upper bound on training error is also provided. Experiment results are shown to demonstrate its cost-sensitivity when selecting weak classifiers, and also show that it outperforms previously proposed cost-sensitive extensions of Discrete Adaboost(DAB) and several extensions of Real Adaboost. Besides, it also consumes much less time than previously proposed DAB extensions.

Cited by 5

AdaBoost with Stumps

2019

This chapter introduces a particular machine learning technique called AdaBoost and applies it on stumps. It provides a working example of the AdaBoost technique with a simple weak learner – a binary decision stump. A decision stump is a one-level decision tree. The rule is formed by a root node and two child nodes. Each node corresponds to a class predicted by the rule based on the one-dimensional feature split according to a chosen threshold. The implementation of the AdaBoost algorithm selects features randomly. It would be more optimal to select the best feature, using the same criteria as is used for threshold selection. AdaBoost can be wrapped into the cross-validation utility function, which will split the sample into parts and verify the accuracy out-of-sample. The AdaBoost technique can be well-defined for regressions as well. Current functions can be thus extended for regressions.

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ADABOOST WITH SIFT

Ko-chih Wang, Ko-chih Wang · 2007

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