A novel SSA-CatBoost machine learning model for credit rating

Rui-cheng, Yang; Wang, Pucong; Qi, Ji

doi:10.3233/jifs-221652

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Currently, feature selection primarily employs three methods: wrapper methods, embedded methods, and filter methods. Wrapper methods, particularly the RFE algorithm, demonstrate excellent performance in feature selection [49,50], when compared to the other two methods.…”

Section: The Procedures Of the Woa-catboost Modelmentioning

confidence: 99%

“…Based on the findings from the pie chart, certain identification feature parameters unrelated to turbulence, such as departure and destination, were excluded as they did not contribute significantly to the relevance of our identification results. algorithm, demonstrate excellent performance in feature selection [49,50], when compared to the other two methods. RFE is an advanced method that obtains the weights of each feature and progressively eliminates features with lower contributions through multiple iterations, thereby completing the feature selection process.…”

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confidence: 96%

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Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Zhuang,

Li,

Shao

et al. 2024

Applied Sciences

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Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

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Section: The Procedures Of the Woa-catboost Modelmentioning

confidence: 99%

mentioning

confidence: 96%

Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Zhuang,

Li,

Shao

et al. 2024

Applied Sciences

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show abstract

“…First, based on the prior probability distribution of the hyperparameters | , the corresponding distribution of the value-at-risk of the objective function is estimated using TPE, where , , , ⋯ , is the hyperparameter and is the value at risk. The next hyperparameter is selected according to the EI, and the above process is repeated to continuously select the hyperparameters using the posterior distribution of the agent model until the optimal solution is obtained, and the probability distribution of the TPE model is defined as shown in equation (9).…”

Section: Bayesian Optimizationmentioning

confidence: 99%

“…Integrated learning algorithms are widely recognized as excellent predictors, formed by combining multiple weak learners through policies to form strongly supervised models, and have shown great advantages when dealing with large data [7,8], and thus have received widespread attention. The classical gradient boosted decision tree (GBDT) uses a linear combination of basic learners to continuously reduce the residuals generated during the training process through multiple iterations [9], thus achieving the classification purpose. Yu [10] accurately distinguished the lithologic interface between breccia, tuff, and rhyolite using the GBDT model.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recognition Technology in Lithology Based on Multi-parameter Fusion of Logging While Drilling

Liang,

Xiong

2023

JID

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In oil and gas drilling, timely and accurate identification of formation lithology is an important guarantee of drilling safety. Aiming at the problems of inaccurate identification of lithology in drilling by traditional methods, and low efficiency due to the fact that even modern instruments cannot respond to lithology in real time. the Categorical Boost (CatBoost) model was applied to lithology identification in this study. However, since CatBoost uses more hyperparameters in its modeling, it is difficult to optimize model prediction by manually tuning the parameters. Therefore, the introduction of Kernel Principal Component Analysis (KPCA) extracts fewer and more important features from the original data, eliminates the redundant information contained therein, and combines with Bayesian Optimization (BO) algorithm to optimize the hyperparameters during the training process, thus improving the prediction performance of CatBoost. Two experiments were designed to verify the recognition ability of the model, and the final test results of the model showed that the KPCA-BO-CatBoost model proposed in this paper had the best overall performance, and the lithology recognition accuracy reached more than 90%. The model was effective in identifying the formation lithology, realized real-time lithology identification by combining the parameters of logging while drilling, improved the efficiency and accuracy of lithology identification, and was of great significance in guiding the subsequent drilling work.

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“…In general, XGBoost showed satisfactory results in lithology identification. CatBoost is further improved for prediction by using a weighted cross-entropy loss function in training, employing the greedy strategy that effectively improves prediction accuracy, applying ordered boosting, optimizing the gradient bias, and using oblivious trees as the base predictor to reduce the risk of overfitting(John T Hancock 2020; Yang et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recognition Technology in Lithology Based on Multi-parameter Fusion

Liang

Xiong

Yang

et al. 2023

Preprint

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In oil and gas drilling, timely and accurate identification of formation lithology is an important factor in drilling safety. In response to the problems of inaccuracy and low efficiency of complex lithology identification by traditional methods such as elemental crossplot in drilling and logging, the Categorical Boost (CatBoost) model is applied to lithology identification in this study. However, since CatBoost uses more hyperparameters in its modeling, it is difficult to optimize model prediction by manually tuning the parameters. Therefore, the introduction of Kernel Principal Component Analysis (KPCA) extracts fewer and more important features from the original data, eliminates the redundant information contained therein, and combines with Bayesian Optimization (BO) algorithm to optimize the hyperparameters during the training process, thus improving the prediction performance of CatBoost. Two experiments were designed to verify the recognition ability of the model, and the final test results of the model showed that the KPCA-BO-CatBoost model proposed in this study had the best comprehensive performance, and the lithology recognition accuracy reached over 90%. The model is effective in identifying formation lithology, improving the efficiency and accuracy of lithology identification and providing important guidance for subsequent drilling operations.

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A novel SSA-CatBoost machine learning model for credit rating

Cited by 11 publications

References 21 publications

Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Research on Intelligent Recognition Technology in Lithology Based on Multi-parameter Fusion of Logging While Drilling

Research on Intelligent Recognition Technology in Lithology Based on Multi-parameter Fusion

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