Relevant bibliographies by topics / CatBoost algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'CatBoost algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "CatBoost algorithm"

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Kong, Lingchao, Hongtao Liang, Guozhu Liu, and Shuo Liu. "Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA." Sensors 23, no. 15 (2023): 6741. http://dx.doi.org/10.3390/s23156741.

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The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent c
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Babu, Mr M. Jeevan. "Mental Health Prediction Using Catboost Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3449–53. http://dx.doi.org/10.22214/ijraset.2024.59219.

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Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust
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Luo, Mi, Yifu Wang, Yunhong Xie, et al. "Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass." Forests 12, no. 2 (2021): 216. http://dx.doi.org/10.3390/f12020216.

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Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm,
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Zhou, Fangrong, Hao Pan, Zhenyu Gao, et al. "Fire Prediction Based on CatBoost Algorithm." Mathematical Problems in Engineering 2021 (July 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/1929137.

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In recent years, increasingly severe wildfires have posed a significant threat to the safe and stable operation of transmission lines. Wildfire risk assessment and early warning have become an important research topic in power grid risk assessment. This study proposes a fire prediction model on the basis of the CatBoost algorithm to effectively predict the fire point. Five wildfire risk factors, including vegetation factors, meteorological factors, human factors, terrain factors, and land surface temperature, were combined using the feature selection method on the basis of the gradient boostin
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Irfan, Muhammad, A. Alwadie, Muhammad Awais, et al. "Motor Bearings Fault Classification using CatBoost Classifier." Renewable Energy and Power Quality Journal 20 (September 2022): 454–57. http://dx.doi.org/10.24084/repqj20.339.

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Induction motors are used in all industries and are the major element of energy consumption. Faults in motor degrade the motor efficiency and result in more energy consumption. Bearing faults are reported to be the major reason for the motor breakdown and a lot of papers have been reported to focus on bearing fault diagnostics. However, low classification accuracy is the main hurdle in adopting the available fault classification algorithms. This paper has presented a novel classification algorithm using the Catboost classifier and timedomain features. The developed algorithm was tested on the
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Wang, Dongming, Xing Xu, Xuewen Xia, and Heming Jia. "Interactive 3D Vase Design Based on Gradient Boosting Decision Trees." Algorithms 17, no. 9 (2024): 407. http://dx.doi.org/10.3390/a17090407.

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Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use of Interactive Genetic Algorithms (IGAs) for ceramic design is a new approach, but an IGA requires a significant amount of user evaluation, which can result in user fatigue. To overcome this problem, this paper introduces the LightGBM algorithm and the CatBoost algorithm to improve
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Hadianto, Agus, and Wiranto Herry Utomo. "CatBoost Optimization Using Recursive Feature Elimination." Jurnal Online Informatika 9, no. 2 (2024): 169–78. http://dx.doi.org/10.15575/join.v9i2.1324.

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CatBoost is a powerful machine learning algorithm capable of classification and regression application. There are many studies focusing on its application but are still lacking on how to enhance its performance, especially when using RFE as a feature selection. This study examines the CatBoost optimization for regression tasks by using Recursive Feature Elimination (RFE) for feature selection in combination with several regression algorithm. Furthermore, an Isolation Forest algorithm is employed at preprocessing to identify and eliminate outliers from the dataset. The experiment is conducted b
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Qiu, Zhaobin, Ying Qiao, Wanyuan Shi, and Xiaoqian Liu. "A robust framework for enhancing cardiovascular disease risk prediction using an optimized category boosting model." Mathematical Biosciences and Engineering 21, no. 2 (2024): 2943–69. http://dx.doi.org/10.3934/mbe.2024131.

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<abstract> <p>Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and it is of utmost importance to accurately assess the risk of cardiovascular disease for prevention and intervention purposes. In recent years, machine learning has shown significant advancements in the field of cardiovascular disease risk prediction. In this context, we propose a novel framework known as CVD-OCSCatBoost, designed for the precise prediction of cardiovascular disease risk and the assessment of various risk factors. The framework utilizes Lasso regression for feature selection and
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Nguyen, Thuan Minh, Hanh Hong-Phuc Vo, and Myungsik Yoo. "Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach." Sensors 24, no. 11 (2024): 3339. http://dx.doi.org/10.3390/s24113339.

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Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and
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Liu, Kuirong, Guanlin Wang, Dajun Mao, and Junqing Huang. "A Hybrid Fault Early-Warning Method Based on Improved Bees Algorithm-Optimized Categorical Boosting and Kernel Density Estimation." Processes 13, no. 5 (2025): 1460. https://doi.org/10.3390/pr13051460.

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In the context of intelligent manufacturing, equipment fault early-warning technology has become a critical support for ensuring the continuity and safety of industrial production. However, with the increasing complexity of modern industrial equipment structures and the growing coupling of operational states, traditional fault warning models face significant challenges in feature recognition accuracy and adaptability. To address these issues, this study proposes a hybrid fault early-warning framework that integrates an improved bees algorithm (IBA) with a categorical boosting (CatBoost) model
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Dissertations / Theses on the topic "CatBoost algorithm"

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Kinnander, Mathias. "Predicting profitability of new customers using gradient boosting tree models : Evaluating the predictive capabilities of the XGBoost, LightGBM and CatBoost algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19171.

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In the context of providing credit online to customers in retail shops, the provider must perform risk assessments quickly and often based on scarce historical data. This can be achieved by automating the process with Machine Learning algorithms. Gradient Boosting Tree algorithms have demonstrated to be capable in a wide range of application scenarios. However, they are yet to be implemented for predicting the profitability of new customers based solely on the customers’ first purchases. This study aims to evaluate the predictive performance of the XGBoost, LightGBM, and CatBoost algorithms in
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Book chapters on the topic "CatBoost algorithm"

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Ho, Van Lam, and Van Tuan Do. "Building Bad Debt Forecasting Model Using CatBoost Algorithm." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75596-5_16.

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Aarthi, B., N. Jeenath Shafana, Simran Tripathy, U. Sampat Kumar, and K. Harshitha. "Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets." In Intelligent Communication Technologies and Virtual Mobile Networks. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1844-5_14.

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Sandhya Reddy, P., M. S. S. Lakshmi Lavanya, Avala Raji Reddy, D. Kishore Kumar, P. Sravanthi, and M. Shivakumar. "CatBoost Algorithm-Based Motivation Assessment Scheme for Intelligent Tutor System." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0143-1_29.

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Zhang, Lufei, Jinge Guo, Bao Guo, and Jinhu Shen. "Root Cause Analysis of 5G Base Station Faults Based on Catboost Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2124-5_27.

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Shao, Shuangrun, Bingxi Zhao, Xiangen Cui, Yihong Dai, and Beining Bao. "Housing Rental Information Management and Prediction System Based on CatBoost Algorithm - a Case Study of Halifax Region." In Rough Sets. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65668-2_16.

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Kalaiarasi, S., S. Alagumani, and D. S. Manoj Kumar. "Early Detection of Suicidal Speculation Using XGboost Compared Catboost Enable Algorithm for Various Text Encodings on Reddit Data." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4071-3_15.

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Harumy, Henny Febriana, Sri Melvani Hardi, and Muhammad Fajri Al Banna. "EarlyStage Diabetes Risk Detection Using Comparison of Xgboost, Lightgbm, and Catboost Algorithms." In Advanced Information Networking and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57931-8_2.

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Sharna, Nadia Ahmed, and Emamul Islam. "Comparative Analysis of CatBoost Against Machine Learning Algorithms for Classification of Altered NSL-KDD." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1923-5_24.

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Chen, Heng. "Catboost Model Unveiled." In Cryptocurrency Market Forecasting With Catboost Models. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305517125010003.

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This chapter delves into the Catboost algorithm, a machine learning method renowned for its handling of categorical data through gradient boosting techniques. It provides an in-depth analysis of Catboost's capabilities, contrasts it with other machine learning algorithms, and discusses its applications across various industries.
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Fu, Jing. "Real-Time Data Analytics and Predictive Maintenance in Commercial Bank Credit Risk Management." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241096.

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In order to identify customers with default risk and avoid credit risk, the application of real-time data analysis and predictive maintenance in credit risk management of commercial banks has been proposed. This paper will use CatBoost algorithm to study credit risk of credit card. This paper first preprocesses the data of 24 real-time variables, such as credit line, gender, age, education, marital status, repayment amount, repayment status, and bills payable, and selects 19 of them as the input variables of the model to establish a credit card user credit risk prediction model based on CatBoost algorithm. The results show that the accuracy of CatBoost is 91.73%, which is the highest among the five models, and the accuracy of Logistic is 74.39%, which is the lowest among the five models. Compared with other algorithms, CatBoost algorithm has higher classification accuracy for credit default prediction of credit card users. Conclusion: The model based on CatBoost algorithm has higher classification accuracy and can provide reference for commercial banks to predict credit card risk.
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Conference papers on the topic "CatBoost algorithm"

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Bharath, D., Sushant Kumar Singh, Saloni Smriti, and Arun A. "Flood Detection on Adverse Natural Conditions using CatBoost algorithm." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673168.

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Shi, Chuan, Bing Xi, Li Shen, and Can Liu. "Concrete compressive strength prediction model based on RS-Catboost algorithm." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10698947.

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Jovanović, Lucija, Kosta Pavlović, Anđela Raičkovićela, Dražen Raičković, and Stevan Šandi. "OXIOM - Posting Invoices using the CatBoost Algorithm and Embedded Representations." In 2025 29th International Conference on Information Technology (IT). IEEE, 2025. https://doi.org/10.1109/it64745.2025.10929798.

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Yu, Haowei, Lin Zou, Yiying Wei, and Zhiyuan Xiong. "Online Driving Style Analysis Based on Genetic Algorithm and CatBoost." In 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC). IEEE, 2024. https://doi.org/10.1109/icairc64177.2024.10900220.

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Fan, Jiongyan. "Research on Financial Risk Assessment Algorithm Based on CatBoost and SVM." In 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025. https://doi.org/10.1109/citsc64390.2025.00051.

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Gyani, Divyangini, Debomita Ghosh, Sonal, and Tirthadip Ghose. "IoT-Enabled Monitoring and Forecasting for Sustainable Adaptive Irrigation System using CatBoost Algorithm." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10738976.

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Thota, Krishna Kishore, J. S. V. Gopala Krishna, K. Sravani, Bhavani Sankar Panda, Geetanjali Panda, and R. Shiva Shankar. "A Model for Predicting Chronic Renal Failure using CatBoost Classifier Algorithm and XGBClassifier." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00025.

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Singh, Preet, K. R. Ramkumar, and Taniya Hasija. "Harnessing Machine Learning for Water Quality Evaluation: Comparative Analysis of XGBoost and CatBoost Algorithm." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673596.

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Verma, Ayush, Namrata Dhanda, and Kapil Kumar Gupta. "An Optimized Forecasting Approach for Virtual Trade Using a Hybrid Arima and Catboost Algorithm." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11004931.

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Zheng, Qinyuan. "Intelligent prediction of trademark registration appeal outcomes based on natural language processing and CatBoost algorithm." In Fifth International Conference on Telecommunications, Optics, and Computer Science, edited by Witold Pedrycz and Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3067906.

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