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

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Gamal, Heba, Nour Eldin Ismail, M. R. M. Rizk, Mohamed E. Khedr, and Moustafa H. Aly. "A Coherent Performance for Noncoherent Wireless Systems Using AdaBoost Technique." Applied Sciences 9, no. 2 (2019): 256. http://dx.doi.org/10.3390/app9020256.

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Boosting is a machine learning approach built upon the idea of producing a highly precise prediction rule by combining many relatively weak and imprecise rules. The Adaptive Boosting (AdaBoost) algorithm was the first practical boosting algorithm. It remains one of the most broadly used and studied, with applications in many fields. In this paper, the AdaBoost algorithm is utilized to improve the bit error rate (BER) of different modulation techniques. By feeding the noisy received signal into the AdaBoost algorithm, it is able to recover the transmitted data from the noisy signal. Consequentl

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Jang, Seok-Woo, and Sang-Hong Lee. "Harmful Content Detection Based on Cascaded Adaptive Boosting." Journal of Sensors 2018 (October 21, 2018): 1–12. http://dx.doi.org/10.1155/2018/7497243.

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Recently, it has become very easy to acquire various types of image contents through mobile devices with high-performance visual sensors. However, harmful image contents such as nude pictures and videos are also distributed and spread easily. Therefore, various methods for effectively detecting and filtering such image contents are being introduced continuously. In this paper, we propose a new approach to robustly detect the human navel area, which is an element representing the harmfulness of the image, using Haar-like features and a cascaded AdaBoost algorithm. In the proposed method, the ni

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Irma Prianti, Ade. "Pebandingan Metode K-Nearest Neighbor dan Adaptive Boosting pada Kasus Klasifikasi Multi Kelas." J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika 13, no. 1 (2020): 39–47. http://dx.doi.org/10.36456/jstat.vol13.no1.a3269.

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Kesehatan keuangan perusahaan memberikan suatu indikasi kinerja perusahaan yang berguna untuk mengetahui posisi perusahaan dalam area industri. Kinerja perusahaan perlu diprediksi untuk mengetahui perkembangan perusahaan. K-Nearest Neighbor (KNN) dan Adaptive Boosting (AdaBoost) merupakan metode klasifikasi yang dapat digunakan untuk memprediksi kinerja perusahaan. KNN mengklasifikasikan data berdasarkan kedekatan jarak data sedangkan AdaBoost bekerja dengan konsep memberi bobot lebih pada amatan yang termasuk weak learner. Tujuan dari penelitian ini adalah membandingkan metode KNN dan AdaBoos

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Mortara, Alda Amalia, Mitta Permatasari, Anita Desiani, Yuli Andriani, and Muhammad Arhami. "Perbandingan Algoritma C4.5 dan Adaptive Boosting dalam Klasifikasi Penyakit Alzheimer." Jurnal Teknologi dan Informasi 13, no. 2 (2023): 196–207. http://dx.doi.org/10.34010/jati.v13i2.10525.

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Penyakit alzheimer adalah penyakit yang menyerang sistem saraf di dalam otak. Penyakit ini dapat menyebabkan terganggunya aktivitas sehari-hari, ingatan yang tidak terorganisir, dan berkurangnya daya ingat. Deteksi dini penyakit alzheimer dapat memanfaatkan pendekatan matematis menggunakan data mining. Data mining memiliki model-model klasifikasi yang dapat digunakan untuk mendeteksi dini penyakit alzheimer. Beberapa algoritma yang dapat digunakan untuk klasifikasi diantaranya adalah C4.5 dan Adaptive Boosting (AdaBoost) yang diterapkan pada penelitian ini untuk mengklasifikasikan penyakit alz

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Seo, Youngmin, Kwanghyun Choi, Yuseong Lim, Byungjoon Lee, and Yunyoung Choi. "Application of Machine Learning Models for Water Pipeline Leakage Detection." Crisis and Emergency Management: Theory and Praxis 19, no. 4 (2023): 45–54. http://dx.doi.org/10.14251/crisisonomy.2023.19.4.45.

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The applicability of machine learning models for detecting water pipeline leakage was evaluated in this study. The machine learning models, extreme gradient boosting (XGBoost), gradient boosting machine (GBM), LightGBM, categorical boosting (CatBoost), adaptive boosting (AdaBoost), and random forest (RF) models, which were developed using the open dataset of water pipeline leakage detection, were evaluated and compared based on classification performance using confusion matrix and performance indices. The results show that the latest boosting models, XGBoost, GBM, LightGBM, and CatBoost, yield

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Riswandhana, Wahyu Aji Tri, and Alva Hendi Muhammad. "Optimalisasi Akurasi Algoritma C4.5 dengan Metode Adaptive Boosting Memprediksi Siswa dalam Menerima Dana Pendidikan." G-Tech: Jurnal Teknologi Terapan 8, no. 4 (2024): 2895–902. http://dx.doi.org/10.70609/gtech.v8i4.5612.

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Pentingnya peningkatan akurasi bagi institusi pendidikan dalam memprediksi pemberian bantuan dana pendidikan. Untuk membuat keputusan tentang siapa yang layak mendapatkan dana pendidikan. Pengolahan data penerima bantuan dapat diolah menjadi informasi. Studi ini bertujuan untuk meningkatkan akurasi algoritma C4.5 dengan menggunakan adaboost untuk menentukan apakah siswa layak mendapatkan dana bantuan pendidikan atau tidak dengan melakukan perbandingan hasil sebelum dan sesudah penerapan adaboost. Prediksi kelayakan siswa dalam memperoleh dana bantuan pendidikan dengan pohon keputusan. Dataset

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Sudarto, Sudarto, Muhammad Zarlis, and Pahala Sirait. "Integrasi Density Based Feature Selection dan Adaptive Boosting dalam Mengatasi Ketidakseimbangan Kelas." Jurnal SIFO Mikroskil 17, no. 2 (2016): 193–206. http://dx.doi.org/10.55601/jsm.v17i2.336.

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Ketidakseimbangan kelas (Class Imbalance) dari dataset antara dua kelas yang berbeda yaitu kelas mayoritas dan kelas minoritas, berpengaruh pada algoritma C4.5 yang cenderung menghasilkan akurasi prediksi yang baik pada kelas mayoritas tetapi??? menjadi tidak konduktif dalam memprediksi contoh kelas minoritas, sehingga nilai hasil akurasi pengklasifikasian (classifier) C4.5 menjadi tidak optimal. Untuk mengurangi pengaruh ketidakseimbangan kelas pada pengklasifikasi C4.5, maka perlu dilakukan dengan menerapkan??? kombinasi dari metode seleksi fitur??? yaitu algoritma Adaptive Boosting (Adaboos

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Wang, Jinghui, and Shugang Tang. "Time series classification based on arima and adaboost." MATEC Web of Conferences 309 (2020): 03024. http://dx.doi.org/10.1051/matecconf/202030903024.

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In this paper, a novel time series classification approach, which using auto regressive integrated moving average model (ARIMA) features and Adaptive Boosting (AdaBoost) classifications. ARIMA is particularly suitable for distinguishing time series signal and Adaboost is suitable for features classification. The simulation results have shown that the algorithm is feasible. And this method is more accurate than many existing method in multiple time series problems.

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Tariq, Irfan, Qiao Meng, Shunyu Yao, et al. "Adaboost-DSNN: an adaptive boosting algorithm based on deep self normalized neural network for pulsar identification." Monthly Notices of the Royal Astronomical Society 511, no. 1 (2022): 683–90. http://dx.doi.org/10.1093/mnras/stac086.

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ABSTRACT A modern pulsar survey generates a large number of pulsar candidates. Filtering these pulsar candidates in a large astronomical data set is an important step towards discovering new pulsars. In this paper, a novel adaptive boosting algorithm based on deep self normalized neural network (Adaboost-DSNN) is proposed to accurately classify pulsar and non-pulsar signals. To train the proposed method on a highly imbalanced data set, the Synthetic Minority Oversampling TEchnique (SMOTE) was initially employed for balancing the data set. Then, a deep ensemble network combined with a deep self

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Putri, Tita Aulia Edi, Tatik Widiharih, and Rukun Santoso. "PENERAPAN TUNING HYPERPARAMETER RANDOMSEARCHCV PADA ADAPTIVE BOOSTING UNTUK PREDIKSI KELANGSUNGAN HIDUP PASIEN GAGAL JANTUNG." Jurnal Gaussian 11, no. 3 (2022): 397–406. http://dx.doi.org/10.14710/j.gauss.11.3.397-406.

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Heart failure is the number one cause of death every year. Heart failure is a pathological condition characterized by abnormalities in heart function, which results in the failure of blood to be pumped to supply metabolic needs of tissues. The application of data mining and computational techniques to medical records can be an effective tool to predict each patient's survival who has heart failure symptoms. Data mining is a process of gathering important information from big data. The collection of important information is carried out through several processes, including statistical methods, m

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Ahmad, Mahmood, Herda Yati Katman, Ramez A. Al-Mansob, Feezan Ahmad, Muhammad Safdar, and Arnold C. Alguno. "Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier." Complexity 2022 (May 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/6156210.

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Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compress

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Elena, Felice, Robyn Irawan, and Benny Yong. "APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 3 (2025): 1957–72. https://doi.org/10.30598/barekengvol19iss3pp1957-1972.

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A service provider is a business that provides services or the expertise of an individual in a certain sector. A service provider’s customer flow could be very dynamic, with both new and churning customers. For the purpose of minimizing the number of churning customers, the company should perform a customer churn analysis. Customer churn analysis is the process of identifying a pattern or trend in churning customers. In order to classify and predict churning customers, machine learning techniques are required to build the classifier model. This paper will use the Support Vector Machine (SVM),

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Onoma, Paul Avweresuo, Joy Agboi, Victor Ochuko Geteloma, et al. "Investigating an Anomaly-based Intrusion Detection via Tree-based Adaptive Boosting Ensemble." Journal of Fuzzy Systems and Control 3, no. 1 (2025): 90–97. https://doi.org/10.59247/jfsc.v3i1.279.

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The eased accessibility, mobility, and portability of smartphones have caused the consequent rise in the proliferation of users' vulnerability to a variety of phishing attacks. Some users are more vulnerable due to factors like personality behavioral traits, media presence, and other factors. Our study seeks to reveal cues utilized by successful attacks by identifying web content as genuine and malicious data. We explore a sentiment-based extreme gradient boost learner with data collected over social platforms, scraped using the Python Google Scrapper. Our results show AdaBoost yields a predic

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Li, Yuan. "Quantum AdaBoost algorithm via cluster state." International Journal of Modern Physics B 31, no. 06 (2017): 1750040. http://dx.doi.org/10.1142/s0217979217500400.

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The principle and theory of quantum computation are investigated by researchers for many years, and further applied to improve the efficiency of classical machine learning algorithms. Based on physical mechanism, a quantum version of AdaBoost (Adaptive Boosting) training algorithm is proposed in this paper, of which purpose is to construct a strong classifier. In the proposed scheme with cluster state in quantum mechanism is to realize the weak learning algorithm, and then update the corresponding weight of examples. As a result, a final classifier can be obtained by combining efficiently weak

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Tsehay, Admassu Assegie, Lakshmi Tulasi R., and Komal Kumar N. "Breast cancer prediction model with decision tree and adaptive boosting." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 184–90. https://doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased t

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Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased t

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Li, Ping, Zichen Zhang, and Jiming Gu. "Prediction of Concrete Compressive Strength Based on ISSA-BPNN-AdaBoost." Materials 17, no. 23 (2024): 5727. http://dx.doi.org/10.3390/ma17235727.

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Strength testing of concrete mainly relies on physical experiments, which are not only time-consuming but also costly. To solve this problem, machine learning has proven to be a promising technological tool in concrete strength prediction. In order to improve the accuracy of the model in predicting the compressive strength of concrete, this paper chooses to optimize the base learner of the ensemble learning model. The position update formula in the search phase of the sparrow search algorithm (SSA) is improved, and piecewise chaotic mapping and adaptive t-distribution variation are added, whic

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Heba Gamal, Nour Eldin Ismail, M. R. M. Rizk, Mohamed E. Khedr, and Moustafa H. Aly. "AdaBoost Algorithm-Based Channel Estimation: Enhanced Performance." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 3 (2023): 296–306. http://dx.doi.org/10.37934/araset.32.3.296306.

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A combination of a group of rules characterized by weakness and imprecision, to reach a prediction rule known for its high precision, forms the concept of boosting, a machine learning approach. From this concept, the Adaptive Boosting (AdaBoost) algorithm spun. It is the first of its kind, and remains in use, under study, and involved in practical applications in various fields to this day. This algorithm is involved in modulation techniques, as it works on improving the bit error rate (BER). Inputting a noisy signal received from a sender into AdaBoost yields the original signal after removin

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Ramadhani, Eva Fadilah, Adji Achmad Rinaldo Fernandes, and Ni Wayan Surya Wardhani. "Comparison of Discriminant Analysis and Adaptive Boosting Classification and Regression Trees on Data with Unbalanced Class." WSEAS TRANSACTIONS ON MATHEMATICS 20 (December 14, 2021): 650–56. http://dx.doi.org/10.37394/23206.2021.20.69.

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This study aims to determine the best classification results among discriminant analysis, CART, and Adaboost CART on Bank X's Home Ownership Credit (KPR) customers. This study uses secondary data which contains notes on the 5C assessment (Collateral, Character, Capacity, Condition, Capital) and collectibility of current and non-current loans. The sample used in this study was from 2000 debtors. Comparison of classifications based on model accuracy, sensitivity, and overall specificity shows that Adaboost CART is the best method for classifying credit collectibility at Bank X. This is due to th

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Bei, Honghan, Yajie Wang, Zhaonuo Ren, Shuo Jiang, Keran Li, and Wenyang Wang. "A Statistical Approach to Cost-Sensitive AdaBoost for Imbalanced Data Classification." Mathematical Problems in Engineering 2021 (October 23, 2021): 1–20. http://dx.doi.org/10.1155/2021/3165589.

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To address the imbalanced data problem in classification, the studies of the combination of AdaBoost, short for “Adaptive Boosting,” and cost-sensitive learning have shown convincing results in the literature. The cost-sensitive AdaBoost algorithms are practical since the “boosting” property in AdaBoost can iteratively enhance the small class of the cost-sensitive learning to solve the imbalanced data issue. However, the most available cost-sensitive AdaBoost algorithms are heuristic approaches, which are improved from the standard AdaBoost algorithm by cost-sensitively adjusting the voting we

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Saputro, Dewi Retno Sari, Krisna Sidiq, Harun Al Rasyid, and Sutanto Sutanto. "TEXT CLASSIFICATION USING ADAPTIVE BOOSTING ALGORITHM WITH OPTIMIZATION OF PARAMETERS TUNING ON CABLE NEWS NETWORK (CNN) ARTICLES." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 1297–306. http://dx.doi.org/10.30598/barekengvol18iss2pp1297-1306.

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The development of the era encourages advances in communication and information technology. This resulted in the exchange of information being faster because it is connected to the internet. One platform that provides online news articles is Cabel News Network (CNN), which has been broadcasting news on its website since 1995. The number of Cabel News Network news articles continues to increase, so news articles are categorized to make it easier for readers to find articles according to the category they want. Classification is a technique for determining the class of an object based on its cha

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Li, Kewen, Guangyue Zhou, Jiannan Zhai, Fulai Li, and Mingwen Shao. "Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data." Sensors 19, no. 6 (2019): 1476. http://dx.doi.org/10.3390/s19061476.

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The Adaptive Boosting (AdaBoost) algorithm 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 algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error cal

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Xue, Guanghui, Peng Hou, Sanxi Li, Xiaoling Qian, Sicong Han, and Song Gao. "Coal Gangue Recognition during Coal Preparation Using an Adaptive Boosting Algorithm." Minerals 13, no. 3 (2023): 329. http://dx.doi.org/10.3390/min13030329.

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: The recognition of coal and gangue is the premise and foundation of coal gangue intelligent sorting. Adaptive boosting (AdaBoost) algorithm-based coal gangue identification has not been studied in depth. This paper proposed a coal gangue image recognition algorithm and a strong classifier based on the AdaBoost algorithm with a genetic algorithm (GA)-optimized support vector machine (SVM). One thousand coal gangue images were collected on-site and expanded to five thousand via rotation and exposure adjustment. The 12 gray-level gradient co-occurrence matrix texture features of the images were

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Lamba, Rohit, Pooja Rani, Ravi Kumar Sachdeva, et al. "An Optimized Predictive Machine Learning Model for Lung Cancer Diagnosis." Biomedical and Pharmacology Journal 18, December Spl Edition (2025): 85–98. https://doi.org/10.13005/bpj/3075.

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Lung cancer is one of the leading causes of death worldwide. Increasing patient survival rates requires early detection. Traditional methods of diagnosis often result in late-stage detection, necessitating the development of more advanced and accurate predictive models. This paper has proposed a methodology for lung cancer prediction using machine learning models. Synthetic minority over-sampling technique (SMOTE) is used before classification to resolve the problem of class imbalance. Bayesian optimization is used to enhance model’s performance. Performance of three classifiers adaptive boost

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Adeboye, Nureni Olawale, and Olawale Victor Abimbola. "An overview of cardiovascular disease infection: A comparative analysis of boosting algorithms and some single based classifiers." Statistical Journal of the IAOS 36, no. 4 (2020): 1189–98. http://dx.doi.org/10.3233/sji-190609.

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Machine learning is a branch of artificial intelligence that helps machines learn from observational data without being explicitly programmed and its methods have been found to be very useful in the modern age for medical diagnosis and for early detection of diseases. According to the World Health Organization, 12 million deaths occur annually due to heart-related diseases. Thus, its early detection and treatment are of interest. This research introduces a better way of improving the timely prediction of cardiovascular diseases in suspected patients by comparing the efficiency of two boosting

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Taser, Pelin Yildirim. "Application of Bagging and Boosting Approaches Using Decision Tree-Based Algorithms in Diabetes Risk Prediction." Proceedings 74, no. 1 (2021): 6. http://dx.doi.org/10.3390/proceedings2021074006.

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Diabetes is a serious condition that leads to high blood sugar and the prediction of this disease at an early stage is of great importance for reducing the risk of some significant diabetes complications. In this study, bagging and boosting approaches using six different decision tree-based (DTB) classifiers were implemented on experimental data for diabetes prediction. This paper also compares applied individual implementation, bagging, and boosting of DTB classifiers in terms of accuracy rates. The results indicate that the bagging and boosting approaches outperform the individual DTB classi

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Fraiwan, Luay, and Omnia Hassanin. "Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers." PLOS ONE 16, no. 6 (2021): e0252380. http://dx.doi.org/10.1371/journal.pone.0252380.

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This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on s

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Abrori, Mohammad Ahmad Maidanul, Abdul Syukur, Affandy Affandy, and Moch Arief Soeleman. "Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding." Journal of Development Research 6, no. 2 (2022): 137–40. http://dx.doi.org/10.28926/jdr.v6i2.205.

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The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree

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Lukas, Samuel, Osvaldo Vigo, Dion Krisnadi, and Petrus Widjaja. "PERBANDINGAN PERFORMA BAGGING DAN ADABOOST UNTUK KLASIFIKASI DATA MULTI-CLASS." Journal Information System Development (ISD) 7, no. 2 (2022): 7. http://dx.doi.org/10.19166/isd.v7i2.547.

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Salah satu teknik untuk meningkatkan performa algoritma Machine Learning adalah menggunakan Ensemble Learning. Ide teknik ini menggabungkan beberapa algoritma Machine Learning atau yang biasa disebut sebagai base learners. Tujuan penlitian ini adalah membandingkan dua performa algoritma Ensemble Learning yaitu metode Bootstrap Aggregating (Bagging) dan metode Adaptive Boosting (AdaBoost). Penelitian menggunakan sebelas dataset dengan klasifikasi multi-class yang independen terhadap karakteristik (proporsi data, jumlah data, dan masalah) serta jumlah kelas variabel target berbeda. Hasil penelit

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Tao, Hongli. "Exploring the impact of data analysis on identifying key predictors of student performance and improving outcomes for diverse groups." Journal of Computational Methods in Sciences and Engineering 25, no. 2 (2024): 1811–25. https://doi.org/10.1177/14727978241305756.

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Identifying key forecasters of student performance is important for improving well-organized educational strategies. Traditional studies commonly focus on standardized student populations, but thoughtful performance predictors between diverse groups can considerably enhance outcomes for all learners. This study aims to rigorously examine student performance by enhancing a novel data-driven strategy that identifies and analyzes key predictors across diverse educational groups, with the ultimate goal of improving educational outcomes for all learners. Data is collected from various educational s

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Khan, Adeel, Irfan Tariq, Haroon Khan, et al. "Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network." Journal of Oncology 2022 (September 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/5682451.

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Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolutio

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Yoo, Gilsang, Hyeoncheol Kim, and Sungdae Hong. "Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network." Bioengineering 10, no. 3 (2023): 361. http://dx.doi.org/10.3390/bioengineering10030361.

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In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG

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Maulana, Azka, Sarwido Sarwido, and Adi Sucipto. "OPTIMASI ALGORITMA SUPPORT VECTOR MACHINES(SVM) MENGGUNAKAN ADAPTIVE BOOSTING(ADABOOST) UNTUK MENINGKATKAN AKURASI PREDIKSI PENYAKIT BRAIN STROKE." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 5 (2025): 7614–20. https://doi.org/10.36040/jati.v9i5.14779.

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Stroke merupakan salah satu penyakit kronis dengan tingkat kematian dan kecacatan yang tinggi, sehingga deteksi dini sangat diperlukan untuk meminimalkan risiko yang ditimbulkan. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi penyakit stroke dengan mengoptimalkan algoritma Support Vector Machines (SVM) menggunakan pendekatan Adaptive Boosting (AdaBoost). Algoritma SVM dikenal efektif untuk klasifikasi data non-linear, namun memiliki keterbatasan saat menangani data yang tidak seimbang. Oleh karena itu, dalam penelitian ini dilakukan preprocessing data meliputi imputasi data hilan

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Nabipour, M., P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and Shahab S. "Deep Learning for Stock Market Prediction." Entropy 22, no. 8 (2020): 840. http://dx.doi.org/10.3390/e22080840.

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The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were ut

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Muhammad Nadim Mubaarok, Triando Hamonangan Saragih, Muliadi, Fatma Indriani, Andi Farmadi, and Achmad Rizal. "Comparison of the Adaboost Method and the Extreme Learning Machine Method in Predicting Heart Failure." Journal of Electronics, Electromedical Engineering, and Medical Informatics 6, no. 3 (2024): 253–63. https://doi.org/10.35882/jeeemi.v6i3.440.

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Heart disease, which is classified as a non-communicable disease, is the main cause of death every year. The involvement of experts is considered very necessary in the process of diagnosing heart disease, considering its complex nature and potential severity. Machine Learning Algorithms have emerged as powerful tools capable of effectively predicting and detecting heart diseases, thereby reducing the challenges associated with their diagnosis. Notable examples of such algorithms include Extreme Learning Machine Algorithms and Adaptive Boosting, both of which represent Machine Learning techniqu

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Cao, Hui, Aiai Wang, Erol Yilmaz, and Shuai Cao. "Machine Learning Algorithm-Based Prediction Model and Software Implementation for Strength Efficiency of Cemented Tailings Fills." Minerals 15, no. 4 (2025): 405. https://doi.org/10.3390/min15040405.

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A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: the age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), additive concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) was applied to existing AI and soft computing techniques, using AdaBoost, random forest (RF), SVM, and ANN. Data were arbitrarily separated into training (70%) and test (30%) sets. Results confirm that AdaB

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Aprihartha, Moch Anjas, Salwa Paramita Azzahro, Rahmatul Azizah, and Muhammad Rafly Andrianza. "Comparison of Discrete Adaptive Boosting Algorithms for Classification and Regression Tree and Naive Bayes in Pistachio Nut Classification." International Journal of Engineering Technology and Natural Sciences 7, no. 1 (2025): 28–36. https://doi.org/10.46923/ijets.v7i1.396.

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Machine learning is an effective tool for identifying and classifying various conditions, such as predicting shoe sales, classifying raisin types, classifying fruit productivity, and so on. This technique is widely used in various sectors. One example is pistachio sorting. In some places, pistachio sorting is still done traditionally by humans. This is disadvantageous because the costs tend to be high, and the sorting process becomes inconsistent and less effective. The use of machine learning algorithms can be a breakthrough in overcoming this problem. Naive Bayes and Classification and Regre

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Gao, Feng, Yage Xing, Jialong Li, et al. "Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables." Molecules 30, no. 7 (2025): 1543. https://doi.org/10.3390/molecules30071543.

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Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score norma

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Wu, Xiaohong, Ziteng Yang, Yonglan Yang, Bin Wu, and Jun Sun. "Geographical Origin Identification of Chinese Red Jujube Using Near-Infrared Spectroscopy and Adaboost-CLDA." Foods 14, no. 5 (2025): 803. https://doi.org/10.3390/foods14050803.

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Red jujube is a nutritious food, known as the “king of all fruits”. The quality of Chinese red jujube is closely associated with its place of origin. To classify Chinese red jujube more correctly, based on the combination of adaptive boosting (Adaboost) and common vectors linear discriminant analysis (CLDA), Adaboost-CLDA was proposed to classify the near-infrared (NIR) spectra of red jujube samples. In the study, the NIR-M-R2 spectrometer was employed to scan red jujube from four different origins to acquire their NIR spectra. Savitzky–Golay filtering was used to preprocess the spectra. CLDA

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Liang, Yun-Chia, Yona Maimury, Angela Hsiang-Ling Chen, and Josue Rodolfo Cuevas Juarez. "Machine Learning-Based Prediction of Air Quality." Applied Sciences 10, no. 24 (2020): 9151. http://dx.doi.org/10.3390/app10249151.

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Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vec

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