Tree-based machine learning methods for predicting vehicle insurance claim size

Material decomposition allows the reconstruction of material-specific images in spectral X-ray imaging, which requires efficient decomposition models. Due to the presence of nonideal effects in X-ray imaging systems, it is difficult to explicitly estimate the imaging systems for material decomposition tasks. As an alternative to previous empirical material decomposition methods, we investigated material decomposition using ensemble learning methods in this paper. Three ensemble methods with two decision trees as the base learning algorithms were investigated to perform material decomposition in both simulation and experiment. The results were quantitatively evaluated for comparison studies. In general, the results demonstrate that the proposed ensemble learning methods often outperform their base learning algorithms, and rarely reduce performance. Compared to the reference methods and its base learning algorithm, the performance of the Boosting method using REPTree with regularization is improved by over 42% and 13%, respectively, in the noiseless simulated scenario of the XCAT phantom with cardiac and respiratory motion, and over 36% and 17%, respectively, in the noisy scenario. Simultaneously, the performance is improved by over 9% and 8%, respectively, in the original torso phantom scenario, and over 13% and 12%, respectively, in the denoising scenario. The results indicate that ensemble learning with gradient descent optimization algorithms is more appropriate for material decomposition tasks. Further studies are encouraged to facilitate future efforts toward clinical applications.

Multi-model ensemble learning for battery state-of-health estimation: Recent advances and perspectives

Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Perfume identification (PI) based on an electronic nose (EN) can be used for exposing counterfeit perfumes more time-efficiently and cost-effectively than using gas chromatography and mass spectrometry instruments. During the past five years, decision-tree-based ensemble learning methods, also called tree ensemble learning methods, have demonstrated excellent performance when solving multi-class classification problems. However, the performance of tree ensemble learning methods for the EN-based PI problem remains uncertain. In this paper, four well-known tree ensemble learning classification methods, random forest (RF), stagewise additive modeling using a multi-class exponential loss function (SAMME), gradient-boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), were implemented for PI using our self-designed EN. For fair comparison, all the tested classification methods used as input the same feature data extracted using principal component analysis. Moreover, two benchmark methods, neural network and support vector machine, were also tested with the same experimental setup. The quantitative results of experiments undertaken demonstrated that the mean PI accuracy achieved by XGBoost was up to 97.5%, and that XGBoost outperformed other tested methods in terms of accuracy mean and variance based on our self-designed EN.

Corrigendum to ‘Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars'

This chapter introduces ensemble learning and provides an overview of ensemble methods for class imbalance learning (CIL). Ensemble methods use a set of classifiers to make predictions. The generalization ability of an ensemble is usually much stronger than that of the individual ensemble members. In CIL, ensemble methods are broadly used to further improve the existing methods or help design brand new ones. According to how the base learners are generated, ensemble methods can be roughly categorized into two paradigms: parallel ensemble methods and sequential ensemble methods. According to what ensemble method is involved, ensemble methods for CIL can be roughly categorized into Bagging-style methods, boosting-based methods, hybrid ensemble methods, and other methods. Most of the ensemble methods for CIL deal with binary class problems. How to use ensemble learning to help multiclass problems is an interesting direction.

The rapid growth of wind energy as a renewable power source has presented both opportunities and operational challenges due to its inherent intermittency and non-linearity. Accurate short-term forecasting of wind power output is vital for improving grid reliability, reducing operating costs, and enhancing energy market efficiency. This study presents a comprehensive comparative analysis of traditional time series models and modern machine learning algorithms for short-term wind power forecasting. Using high-frequency SCADA data from a commercial wind turbine in Turkey, we evaluate the predictive performance of nine models: Seasonal Naive, Exponential Smoothing (ETS), ARIMA, ARIMAX, Dynamic Harmonic Regression, Linear Regression, Gradient Boosted Trees (GBM), and Generalized Additive Models (GAM). Root Mean Square Error (RMSE) is employed as the primary accuracy metric on a hold-out test set. Among the models analyzed, the Gradient Boosted Tree model demonstrated superior performance with the lowest RMSE of 16.05, followed by ARIMAX (RMSE = 20.8), GAM (RMSE = 29.7), and linear regression using external meteorological inputs (RMSE = 29.9). Traditional statistical models such as ETS and ARIMA showed comparatively lower performance, particularly in handling non-linear patterns and multiple seasonalities. The study highlights the importance of integrating exogenous variables—such as wind speed, direction, and theoretical power curve—into forecasting frameworks to capture complex relationships and improve accuracy. Our findings emphasize that ensemble learning methods and hybrid statistical-ML approaches offer meaningful advancements in renewable energy forecasting. These insights provide valuable guidance for energy planners, system operators, and stakeholders seeking to optimize renewable integration and grid stability. Future work can explore deep learning architectures and spatiotemporal models across multi-turbine datasets for broader applicability.

The silicon content of the hot metal is not only an important indicator of the quality of the hot metal and blast furnace (BF) operation but also reflects the level of energy utilization and the thermal state within a BF. It is important to develop an accurate prediction model for hot metal silicon content. In present study, two models for predicting hot metal silicon content are developed based on two ensemble learning methods, random forest regression (RFR) and extreme gradient boosting (XGBoost). First, Box plot was used to visualize the collected data and determine extreme outliers in the raw data. Extreme outliers are replaced with null value, and all null value are filled by linear interpolation. Secondly, feature selection is performed using recursive feature elimination. Cross-validation is performed to optimize machine learning hyperparameters and having a robust accuracy measure. Based on this, two hot metal silicon content prediction models are developed. Finally, the prediction results of the two models are compared and evaluated. The results show that both ensemble learning models show good prediction performance in predicting hot metal silicon content, but the prediction performance of the RFR model is better than that of the XGBoost model and reaching 98.77%.

Child mortality is a big problem around the world, especially in low- and middle-income nations where there are big differences in health care and social conditions. This investigation seeks to create a predictive model for child mortality and pinpoint the key factors that significantly contribute to it, employing machine learning (ML) methodologies. The dataset includes various features such as parental age, maternal education, birth weight, wealth index, and access to healthcare services. Thirteen machine learning classifiers were used, categorized into four model groups: Traditional Models (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes), Tree-Based Models (Decision Tree, Random Forest, Extra Trees), Boosting Models (AdaBoost, Gradient Boosting, XGBoost), and Ensemble Learning Models (Soft Voting, Hard Voting, Stacking). The efficacy of each model was assessed using classification metrics, including Accuracy, Precision, Recall, and F1-Score within a 10-fold cross-validation framework to guarantee robustness. Results indicate that ensemble models, particularly AdaBoost, achieved the highest predictive accuracy, with perfect scores across all metrics (1.00). XGBoost and Stacking also demonstrated strong and consistent performance. The findings indicate that ensemble learning methods are effective in predicting child mortality and can assist policymakers and healthcare planners in identifying high-risk populations and implementing targeted interventions to reduce child mortality.

Extraction of insulators from power corridors is crucial for the construction of smart grids. Recent significant progress has been made in transmission line inspection through the application of Unmanned Aerial Vehicle Light Detection and Ranging (UAV LiDAR) systems, which can efficiently capture three-dimensional structural information. However, accurately extracting insulator components from massive point clouds remains challenging due to their high similarity in shape with surrounding pylons and power lines, as well as the inherent data imbalance issues. After a series of preprocessing steps, numerous multi-scale geometric features are designed and fused to precisely characterize insulators. Considering the issue of data imbalance due to the limited number of insulator samples, an Anomaly-Guided Progressively Balanced Ensemble (APE) Learning method is presented, using anomaly scores to achieve a good balance. Finally, the initial results are refined by incorporating spatial context information. The proposed delicate features and ensemble learning method were verified to be suitable for small insulator target extraction (including tangent insulators and tension insulators of different pylon types), achieving average precision, recall, and F1 scores of up to 95%. The results can promote intelligent inspection and renovation of large-scale power corridors.

Prediction of the stock price has always been a challenging task due to irregular patterns of the market. Uncertainty has made researchers think of some new and robust predictive methods. Many studies are available in the literature, with many models to predict the stock price accurately. Statistical, machine learning, deep learning, and other related approaches can create a predictive model. ARIMA model is the most commonly used statistical model for time series prediction. But ensemble learning techniques have not been explored much to predict future stock price. So, the present study stresses comparing statistical methods with ensemble learning methods. This paper compares the ARIMA, Random Forest, and Extreme Gradient Boosting models based on root mean squared error (RMSE) and mean absolute percentage error (MAPE). The subject chosen is Google’s stocks, and the data used is from NASDAQ stock exchange. The analysis results show that the ARIMA model performed fairly well for short-term predictions but relatively high MAPE value. The extreme gradient boosting model gave the best performance with the lowest RMSE and MAPE value. Hence, it is evident that after proper hyperparameter tuning, ensemble learning techniques can be used to create robust stock price-prediction models.

The evolution of Electronic Health Records (EHR) has facilitated comprehensive patient record-keeping, enhancing healthcare delivery and decision-making processes. Despite these advancements, analyzing EHR data using ensemble machine learning methods poses unique challenges. These challenges include data dimensionality, imbalanced class distributions, and the need for effective hyperparameter tuning to optimize model performance. The study conducted a thorough comparative analysis of various ensemble machine learning (EML) models using Electronic Health Record (EHR) datasets. After addressing data imbalance and reducing dimensionality, the accuracy of the EML models showed significant improvement. Notably, the Gradient Boosting Machine (GBM) and CatBoost models exhibited superior performance with an accuracy of 73%, achieved through experiments involving dimensionality reduction and handling of imbalanced data. Furthermore, optimization techniques such as Grid Search and Random Search were employed to enhance the EML models. The results of model optimization revealed that the GBM + Random Search model performed the best, achieving an accuracy of 74%, followed by the XGBoost + Grid Search model with an accuracy of 73%. The GBM model also excelled in distinguishing between positive and negative classes, boasting the highest Area under Curve (AUC) value of 0.78, indicative of its superior classification capabilities compared to other models. This study emphasizes the significance of incorporating cutting-edge EML techniques into clinical workflows and emphasizes the revolutionary potential of GBM in classification modeling for patient severity conditions. Future research should focus on deep learning (DL) applications and the integration of these models.

Agricultural management and production rely heavily on advancements in technology for tasks such as crop yield forecasting, disease detection, and soil classification. However, machine learning models often encounter challenges related to the complexity and variability of agricultural datasets. This study addresses these challenges by integrating deep learning, ensemble learning methods, and extensive dataset exploration to enhance forecasting accuracy and model robustness. Despite the promise of these approaches, limited research has examined their combined effects on model performance. Our findings reveal significant improvements across various agricultural applications. By combining ensemble methods like Random Forest and Gradient Boosting Machines (GBM) with deep learning, the study achieved a 15% reduction in mean absolute error for irrigation scheduling and a 12% increase in recall for weed detection. These results underscore the potential of integrating modern techniques to optimize agricultural decision-making and improve predictive performance in diverse scenarios.

Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be retrained to attain new models for the current data. Two design questions need to be addressed in developing ensemble methods for incremental learning with concept drift, i.e., which historical (i.e., previously trained) models should be preserved and how to utilize them. A novel ensemble learning method, namely, Diversity and Transfer-based Ensemble Learning (DTEL), is proposed in this paper. Given newly arrived data, DTEL uses each preserved historical model as an initial model and further trains it with the new data via transfer learning. Furthermore, DTEL preserves a diverse set of historical models, rather than a set of historical models that are merely accurate in terms of classification accuracy. Empirical studies on 15 synthetic data streams and 5 real-world data streams (all with concept drifts) demonstrate that DTEL can handle concept drift more effectively than 4 other state-of-the-art methods.