Relevant bibliographies by topics / AdaBoost (Adaptive Boosting)

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Academic literature on the topic 'AdaBoost (Adaptive Boosting)'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AdaBoost (Adaptive Boosting)"

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MENAKA, B., and Dr S. ARULSELVARANI. "Optimizing Cyber Threat Detection Through Bottleneck Feature Extraction and Adaptive Boosting." Indian Journal Of Science And Technology 18, no. 28 (2025): 2246–56. https://doi.org/10.17485/ijst/v18i28.1138.

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Objective: This study aims at optimization of cloud-based cyber threat detectors through the combination of autoencoder based feature compression with the AdaBoost classification algorithm. Its greatest aim is to properly classify different kinds of network attacks with the help of an efficient, broad-based model that uses the AWS Cloud Investigation Dataset as training. The idea is to be as accurate as possible but with minimal overfitting and dealing efficiently with multi-class cases in clouds. Methods: It consists of preprocessing of the dataset by one-hot encoding and feature normalizatio
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Mendrofa, Rosa Delima, Maria Hosianna Siallagan, Junita Amalia, and Diana Pebrianty Pakpahan. "Credit Risk Analysis With Extreme Gradient Boosting and Adaptive Boosting Algorithm." Journal of Information System,Graphics, Hospitality and Technology 5, no. 1 (2023): 1–7. http://dx.doi.org/10.37823/insight.v5i1.233.

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Credit Risk Analysis digunakan untuk mengenali resiko terhadap pinjaman untuk mencegah penunggakan pembayaran utang. Pemberian uji kelayakan pinjaman dapat di analisis menggunakan model klasifikasi. Untuk menghasilkan model credit risk analysis yang sesuai, penulis mengajukan Algoritma Extreme Gradient Boosting (XGBoost) dan Adaptive Boosting (AdaBoost). Data yang digunakan dalam penelitian ini adalah data pinjaman platform Peer to Peer (P2P) Lending. Penelitian ini menerapkan data preprocessing yang bertujuan untuk menghasilkan data yang lebih baik dan melakukan analisis terhadap data. Analis
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Nayab, Durr e., Rehan Ullah Khan, and Ali Mustafa Qamar. "Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets." Applied Computational Intelligence and Soft Computing 2023 (December 22, 2023): 1–10. http://dx.doi.org/10.1155/2023/5542049.

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This paper investigates the performance enhancement of base classifiers within the AdaBoost framework applied to medical datasets. Adaptive boosting (AdaBoost), being an instance of boosting, combines other classifiers to enhance their performance. We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. The experiments are carried out on five datasets from the medical
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Sah, Andrian, Chaeroen Niesa, Rhaishudin Rumandan Jafar, and Muhammad Muharrom. "Analisis Model Prediksi Penyakit Jantung Menggunakan Adaptive Boosting, Gradient Boosting, dan Extreme Gradient Boosting." Jurnal Ilmiah FIFO 17, no. 1 (2025): 46. https://doi.org/10.22441/fifo.2025.v17i1.006.

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Deteksi dini penyakit jantung merupakan langkah penting untuk meningkatkan kualitas diagnosis dan perawatan pasien. Namun, metode prediksi manual yang sering digunakan tenaga medis memiliki keterbatasan dalam efisiensi waktu, akurasi, dan kemampuan menangani volume data yang besar. Dalam bidang kecerdasan buatan, algoritma machine learning seperti Adaptive Boosting (AdaBoost), Gradient Boosting, dan Extreme Gradient Boosting (XGBoost) menawarkan potensi untuk meningkatkan akurasi prediksi, terutama dalam mengatasi tantangan pada dataset kecil yang sering mengalami ketidakseimbangan kelas dan r
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La, Lei, Qiao Guo, Dequan Yang, and Qimin Cao. "Multiclass Boosting with Adaptive Group-BasedkNN and Its Application in Text Categorization." Mathematical Problems in Engineering 2012 (2012): 1–24. http://dx.doi.org/10.1155/2012/793490.

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AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM), neural networks (NN), naïve Bayes, andk-nearest neighbor (kNN). This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-basedkNN method is proposed in this paper to b
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Riansyah, Muhammad, Saib Suwilo, and Muhammad Zarlis. "Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost)." SinkrOn 8, no. 2 (2023): 617–22. http://dx.doi.org/10.33395/sinkron.v8i2.12055.

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The Decision Tree algorithm is a data mining method algorithm that is often applied as a solution to a problem for a classification. The Decision Tree C5.0 algorithm has several weaknesses, including: the C5.0 algorithm and several other decision tree methods are often biased towards modeling whose features have many levels, some problems for the model can occur such as over-fit or under-fit challenges, big changes to decision logic can result in small changes to data training, C5.0 can experience modeling inconvenience, data imbalance causes low accuracy in C5.0 algorithm. The boosting algori
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Zhang, Jiangnan, Kewen Xia, Ziping He, Zhixian Yin, and Sijie Wang. "Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection." Mathematical Problems in Engineering 2021 (February 18, 2021): 1–18. http://dx.doi.org/10.1155/2021/6622935.

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The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost classifier directly to pulmonary nodule detection of labeled and unlabeled lung CT images since there are still some drawbacks to ensemble learning method. Therefore, to solve the labeled and unlabeled data classification problem, the semi-supervised AdaBoost classifier using an improved sparrow search algorithm (AdaBoost-ISSA-S4VM) was established. Firstly, AdaBoost classifier is used to const
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Prianti, Ade Irma, Rukun Santoso, and Arief Rachman Hakim. "PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN ADAPTIVE BOOSTING PADA KASUS KLASIFIKASI MULTI KELAS." Jurnal Gaussian 9, no. 3 (2020): 346–54. http://dx.doi.org/10.14710/j.gauss.v9i3.28924.

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The company's financial health provides an indication of company’s performance that is useful for knowing the company's position in industrial area. The company's performance needs to be predicted to knowing the company's progress. K-Nearest Neighbor (KNN) and Adaptive Boosting (AdaBoost) are classification methods that can be used to predict company's performance. KNN classifies data based on the proximity of the data distance while AdaBoost works with the concept of giving more weight to observations that include weak learners. The purpose of this study is to compare the KNN and AdaBoost met
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Akazue, Maureen, Anthonia Onovughe, Omede Edith, and John Paul A.C. Hampo. "Use of Adaptive Boosting Algorithm to Estimate User's Trust in the Utilization of Virtual Assistant Systems." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 502–7. https://doi.org/10.5281/zenodo.7568675.

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User trust in technology is an essential factor for the usage of a system or machine. AI enabled technologies such as virtual digital assistants simplify a lot of process for humans starting from simple search to a more complex action like house automation and completion of some transitions notably Amazon’s Alexa. Can human actually trust these AI enabled technologies? Hence, this research applied adaptive boosting ensemble learning approach to predict users trust in virtual assistants. A technology trust dataset was obtained from figshare.com and engineered before training the adaptive
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Anita Desiani, Siti Nurhaliza, Tri Febriani Putri, and Bambang Suprihatin. "Algoritma Extreme Gradient Boosting (XGBoost) dan Adaptive Boosting (AdaBoost) Untuk Klasifikasi Penyakit Tiroid." Jurnal Rekayasa Elektro Sriwijaya 6, no. 2 (2025): 66–75. https://doi.org/10.36706/jres.v6i2.145.

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Thyroid disease is a disease of the thyroid gland that can interfere with daily activities. Early detection of thyroid disease can have an important impact in optimizing the development of early detection systems that are more effective and accurate in detecting the disease. Data mining approaches can be used to solve this problem by utilizing various available algorithms, such as Adaptive Boosting and Extreme Gradient Boosting. This research aims to improve the development of early thyroid disease prediction by comparing the two algorithms by utilizing the percentage split method. This resear
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Book chapters on the topic "AdaBoost (Adaptive Boosting)"

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Shi, Heng, Belkacem Chikhaoui, and Shengrui Wang. "Tree-Based Models for Pain Detection from Biomedical Signals." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_14.

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AbstractFor medical treatments, pain is often measured by self-report. However, the current subjective pain assessment highly depends on the patient’s response and is therefore unreliable. In this paper, we propose a physiological-signals-based objective pain recognition method that can extract new features, which have never been discovered in pain detection, from electrodermal activity (EDA) and electrocardiogram (ECG) signals. To discriminate the absence and presence of pain, we establish four classification tasks and build four tree-based classifiers, including Random Forest, Adaptive Boost
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El bakrawy, Lamiaa M., and Abeer S. Desuky. "A Hybrid Classification Algorithm and Its Application on Four Real-World Data Sets." In Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5656-9.ch006.

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The aim of this chapter is to propose a hybrid classification algorithm based on particle swarm optimization (PSO) to enhance the generalization performance of the adaptive boosting (AdaBoost) algorithm. AdaBoost enhances any given machine learning algorithm performance by producing some weak classifiers which requires more time and memory and may not give the best classification accuracy. For this purpose, PSO is proposed as a post optimization procedure for the resulted weak classifiers and removes the redundant classifiers. The experiments were conducted on the basis of ionosphere data set,
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Caceres Hernandez, Danilo, Laksono Kurnianggoro, Alexander Filonenko, and Kang-Hyun Jo. "Obstacle Classification Based on Laser Scanner for Intelligent Vehicle Systems." In Advances in Computational Intelligence and Robotics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9924-1.ch010.

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In the field of advanced driver-assistance and autonomous vehicle systems, understanding the surrounding vehicles plays a vital role to ensure a robust and safe navigation. To solve detection and classification problem, an obstacle classification strategy based on laser sensor is presented. Objects are classified according the geometry, distance range, reflectance, and disorder of each of the detected object. In order to define the best number of features that allows the algorithm to classify these objects, a feature analysis is performed. To do this, the set of features were divided into four
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Ramalingam, Renugadevi. "An Innovative Investigation on Predicting Forest Fire Using Machine Learning Approach." In AI and IoT for Proactive Disaster Management. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3896-4.ch004.

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Predicting forest fire occurrences can bolster early detection capabilities and improve early warning systems and responses. Currently, forest and grassland fire prevention and suppression efforts in China face significant hurdles due to the complex interplay of natural and societal factors. While existing models for predicting forest fire occurrences typically consider factors like vegetation, topography, weather conditions, and human activities, the moisture content of forest fuels is a critical aspect closely linked to fire occurrences. Additionally, it introduces forest fuel-related factor
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Conference papers on the topic "AdaBoost (Adaptive Boosting)"

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Peri, Satya Subrahmanya Sai Ram Gopal, Asokan D, Divya Goel, Anjali Anjali, Nalini Devi S, and Keerthana N. V. "Strategic Leadership and Digital Disruption: A Roadmap for Organizational Expansion using Adaptive Boosting (AdaBoost)." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11080789.

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Okai, M. I., O. Ogolo, P. Nzerem, and K. S. Ibrahim. "Application of Boosting Machine Learning for Mud Loss Prediction During Drilling Operations." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/221583-ms.

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Abstract Lost circulation during drilling operations is a persistent challenge in the oil and gas industry, leading to significant financial losses and increased non-productive time. The common use of lost circulation materials (LCMs) in drilling fluids helps mitigate mud loss only to an extent. However, predicting the extent of mud loss before drilling specific formations would greatly benefit engineers. This study aims to predict mud loss using advanced boosting machine learning frameworks, addressing the need for more accurate forecasting tools. We evaluated three ensemble boosting algorith
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Donat, William, Kihoon Choi, Woosun An, Satnam Singh, and Krishna Pattipati. "Data Visualization, Data Reduction and Classifier Fusion for Intelligent Fault Detection and Diagnosis in Gas Turbine Engines." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-28343.

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In this paper, we investigate four key issues associated with data-driven approaches for fault classification using the Pratt and Whitney commercial dual-spool turbofan engine data as a test case. The four issues considered here include: (1) Can we characterize, a priori, the difficulty of fault classification via self-organizing maps? (2) Do data reduction techniques improve fault classification performance and enable the implementation of data-driven classification techniques in memory-constrained digital electronic control units (DECUs)?, (3) When does adaptive boosting, an incremental fusi
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Jagtap, Shilpa, J. L. Mudegaonkar, Sanjay Patil, and Dinesh Bhoyar. "A Novel Approach for Diagnosis of Diabetes Using Iris Image Processing Technique and Evaluation Parameters." In National Conference on Relevance of Engineering and Science for Environment and Society. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.118.37.

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This paper presented here deals with study of identification and verification approach of Diabetes based on human iris pattern. In the pre-processing of this work, region of interest according to color (ROI) concept is used for iris localization, Dougman's rubber sheet model is used for normalization and Circular Hough Transform can be used for pupil and boundary detection. To extract features, Gabor Filter, Histogram of Oriented Gradients, five level decomposition of wavelet transforms likeHaar, db2, db4, bior 2.2, bior6.8 waveletscan be used. Binary coding scheme binaries’ the feature vector
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Khan, Abdul Muqtadir, Abdullah BinZiad, and Abdullah Al Subaii. "Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205642-ms.

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Abstract With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm. For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently ou
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Al-Mudhafar, Watheq J., and David A. Wood. "Tree-Based Ensemble Algorithms for Lithofacies Classification and Permeability Prediction in Heterogeneous Carbonate Reservoirs." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31780-ms.

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Abstract Rock facies are typically identified either by core analysis to provide visually interpreted lithofacies, or determined indirectly based on suites of recorded well-log data, thereby generating electrofacies interpretations. Since the lithofacies cannot be obtained for all reservoir intervals, drilled section and/or wells, it is commonly essential to model the discrete lithofacies as a function of well-log data (electrofacies) to predict the poorly sampled or non-cored intervals. The process is called predictive lithofacies classification. In this study, measured discrete lithofacies d
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Al-Mudhafar, Watheq J., and David A. Wood. "Tree-Based Ensemble Algorithms for Lithofacies Classification and Permeability Prediction in Heterogeneous Carbonate Reservoirs." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31780-ms.

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Abstract Rock facies are typically identified either by core analysis to provide visually interpreted lithofacies, or determined indirectly based on suites of recorded well-log data, thereby generating electrofacies interpretations. Since the lithofacies cannot be obtained for all reservoir intervals, drilled section and/or wells, it is commonly essential to model the discrete lithofacies as a function of well-log data (electrofacies) to predict the poorly sampled or non-cored intervals. The process is called predictive lithofacies classification. In this study, measured discrete lithofacies d
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Tackie-Otoo, Bennet Nii, Joshua Nsiah Turkson, Mohamed Mahmoud, Arshad Raza, Shirish Patil, and Victor Darkwah-Owusu. "Comparative Analysis of Ensemble Learning, Evolutionary Algorithm, and Molecular Dynamics Simulation for Enhanced Aqueous H2/Cushion Gases Interfacial Tension Prediction: Implications on Underground H2 Storage." In GOTECH. SPE, 2025. https://doi.org/10.2118/224624-ms.

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Abstract There is a strong push for decarbonization in the Global North and South to combat climate change and avert its dire consequences including rising sea levels, severe weather events, biodiversity loss, and threats to food and water security. Hydrogen (H2) emerges as a sustainable energy carrier in this regard. Underground hydrogen storage (UHS) presents significant potential but requires a thorough understanding of H2 behavior in porous media. One of the crucial parameters is interfacial tension (IFT) which influences capillary entry pressure, H2 column height, and storage capacity. Ac
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Al-Sahlanee, Dhuha T., Raed H. Allawi, Watheq J. Al-Mudhafar, and Changqing Yao. "Ensemble Machine Learning for Data-Driven Predictive Analytics of Drilling Rate of Penetration (ROP) Modeling: A Case Study in a Southern Iraqi Oil Field." In SPE Western Regional Meeting. SPE, 2023. http://dx.doi.org/10.2118/213043-ms.

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Abstract Modeling the drill bit Rate of Penetration (ROP) is crucial for optimizing drilling operations as maximum ROP causes fast drilling, reflecting efficient rig performance and productivity. In this paper, four Ensemble machine learning (ML) algorithms were adopted to reconstruct ROP predictive models: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boost (XGB), and Adaptive Boosting (AdaBoost). The research was implemented on well data for the entire stratigraphy column in a giant Southern Iraqi oil field. The drilling operations in the oil field pass through 19 formations (
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Khan, Mohammad Rasheed, Zeeshan Tariq, Muhammad Ali, and Mobeen Murtaza. "Predicting Interfacial Tension in CO2/Brine Systems: A Data-Driven Approach and Its Implications for Carbon Geostorage." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23568-ms.

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Abstract CO2 Interfacial Tension (IFT) and the reservoir rock-fluid interfacial interactions are critical parameters for successful CO2 geological sequestration, where the success relies significantly on the rock-CO2-brine interactions. IFT behaviors during storage dictate the CO2/brine distribution at pore scale and the residual/structural trapping potentials of storage/caprocks. Experimental assessment of CO2-Brine IFT as a function of pressure, temperature, and readily available organic contaminations on rock surfaces is arduous because of high CO2 reactivity and embrittlement damages. Data
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