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Employee Attrition Classification Model Based on stacking algorithm

Psychology Research 2023年第6期13卷 279-285页

作者： CHEN Yanming LIN Xinyu ZHAN Kunye Shantou University South China Normal University Shenzhen University

This paper aims to build an employee attrition classification model based on the stacking *** algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and ***,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to ***,the stacking algorithm is used to establish the final classification *** model has practical and significant implications for both human resource management and employee attrition analysis.

关键词： employee attrition classification model machine learning ensemble learning oversampling algorithm Randomforest stacking algorithm

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A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 122卷

作者： Ma, Zhiyuan Li, Wei Warner, Timothy A. He, Can Wang, Xue Zhang, Yu Guo, Caili Cheng, Tao Zhu, Yan Cao, Weixing Yao, Xia Nanjing Agr Univ Inst Smart Agr Natl Engn & Technol Ctr Informat Agr NETCIA MOE Engn Res Ctr Smart AgrMARA Key Lab Crop Syst 1 Weigang Nanjing 210095 Jiangsu Peoples R China West Virginia Univ Dept Geol & Geog Morgantown WV USA

Mapping crop distribution using satellite technology is an effective approach for gaining information about food production over broad, regional scales. However, crop classification in high altitude regions from satellite platforms remains challenging, due to the spatial heterogeneity caused by the complex planting patterns. Moreover, the frequent cloud cover makes it difficult to collect time-series imagery for these regions. Thus, this study used a mosaic of single images of Gaofen-6 data to map the crop distribution in high altitude regions of Xining City and Haidong City prefectures of Qinghai Province, China. To improve the accuracy of the crop classification, random forest-recursive feature elimination (RF-RFE) was used to determine an optimal feature subset from existing spectral, texture and topographic features. Then, a two-layer stacking generalization ensemble model, incorporating Random Forest, XGBoost and AdaBoost, was trained. The results reveal that the stacking algorithm outperformed the other single classifiers, with overall accuracy higher than 85% (87.89% for the optimal feature subset and 85.38% for the original spectral band subset). In addition, the user's and pro-ducer's accuracies for wheat, rape and maize field all exceeded 90%. Elevation was the variable with the highest importance score, illustrating its importance in crop classification of high altitude regions. Overall, the frame-work, combining RF-RFE and a stacking algorithm, can improve the accuracy of the crop classification in high altitude regions.

关键词： High altitude regions Gaofen-6 Crop classification stacking algorithm

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Enhanced classification of Traditional Chinese Medicine syndromes in primary lung cancer patients using stacking ensemble machine learning algorithms 5

Enhanced classification of Traditional Chinese Medicine synd...

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5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Wang, Mengyao Liu, Jifen Zeng, Meiling Hubei University of Traditional Chinese Medicine Hubei Wuhan 430000 China Affiliated Hospital of Hubei University of Traditional Chinese Medicine Hubei Wuhan 430000 China

ISBN: (纸本)9781510691667

This study aims to employ advanced machine learning techniques, particularly the stacking ensemble algorithm, to accurately classify thirteen Traditional Chinese Medicine (TCM) syndromes in primary lung cancer patients. Data from 2, 598 lung cancer cases were collected from nine hospitals nationwide, encompassing 125 clinical symptoms and signs. Data preprocessing included missing value imputation, feature selection, and standardization to ensure data quality and effective model training. Nine foundational models were trained: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), and Multi-Layer Perceptron (MLP). By selecting the top four performing basic models for the stacking ensemble, classification performance was significantly enhanced. Experimental results demonstrated that the XGBoost fusion model achieved a micro-average Area Under the ROC Curve (AUC) of 0.996, outperforming single models and highlighting the efficiency and stability of ensemble learning in TCM syndrome diagnosis. This study confirms that the stacking ensemble algorithm holds significant potential for complex multi-class classification tasks, offering a novel computational approach for TCM diagnostics. © 2025 SPIE.

关键词： Ensemble learning machine learning model generalization stacking algorithm TCM syndrome classification

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A synchronous retrieval method of vegetation water content and soil moisture based on GNSS-IR and multi-source data fusion

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JOURNAL OF SPATIAL SCIENCE 2025年第1期70卷 161-181页

作者： Ma, Jiajia Liang, Yueji Cheng, Bo Hu, Xinmiao Ren, Chao Yang, Mengmeng Huang, Yibang Liang, Caiwen Guo, Xi Guilin Univ Technol Coll Geomat & Geoinformat Guilin Peoples R China Zhejiang Engn Survey & Design Inst Grp Co Ltd Ningbo Peoples R China Chinese Acad Agr Sci Inst Grassland Res Hohhot Peoples R China

Vegetation Water Content (VWC) and Soil Moisture (SM) are closely related. To overcome the limitations of the Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) for point-based monitoring, this research aimed at developing a synchronous retrieval method for VWC and SM by integrating GNSS-IR and multi-source data. The method incorporates band synthesis, optimized modelling, and a stacking algorithm. The model produced Normalized Microwave Reflectance Index (NMRI) and SM products consistent with MODIS NDVI, LAI, and NASA-USDA SM data in both time and space. The study provides a novel approach for the synchronous retrieval of VWC and SM and facilitates in-depth exploration of their interaction mechanisms.

关键词： GNSS-IR vegetation water content soil moisture stacking algorithm synchronous retrieval

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Design of Electric Energy Metering Detection algorithm Based on stacking Integration Mode 3

Design of Electric Energy Metering Detection Algorithm Based...

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3rd International Conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Wang, Yanru Jilin Univ Ctr Econ Expt Sch Econ Changchun 130021 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

With the gradual advancement of modernization, electric energy plays an increasingly important role in social construction and residents' life. In order to ensure the economic interests of the electric power department and consumers, regular accurate measurement and detection of electric energy in the substation is particularly important. This paper presents an electric energy metering and detection algorithm based on stacking algorithm.

关键词： stacking algorithm Integration Mode Electric Energy Metering Detection

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Transformer Fault Diagnosis Based on stacking Ensemble Learning

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第12期15卷 1734-1739页

作者： Wang, Xue Han, Tao North China Elect Power Univ Dept Elect Engn Baoding 071000 Peoples R China State Grid Jiangsu Elect Power Co Yancheng Power Supply Branch Yancheng 224000 Jiangsu Peoples R China

Dissolved gas analysis is an important way to diagnose transformer faults. Compared with the method of establishing a single classifier based on artificial intelligence for diagnosis, ensemble learning (EL) can combine multiple classifiers to achieve stronger generalization ability and better diagnostic performance. But the traditional EL belongs to homogenous ensemble in which the base learners are based on the same algorithm, so this kind of EL method lacks the differences among the base learners, as well as systematic combination strategy. For this problem, in the paper the stacking ensemble strategy is applied to fault diagnosis. Multilayer perceptron, k-nearest neighbor, decision tree and support vector machine are used as component learners, and random forest algorithm is used as a combination strategy to establish a stacking diagnosis model. In addition, homogenous ensemble methods are applied to the above four algorithms. In the method, the content of five characteristic gases are taken as the input characteristic parameters. Primary diagnostic results can be obtained with each base classifier. Then the meta-learner random forest model organizes the base classifiers, and uses the primary diagnostic output as the input of the meta-learner for secondary diagnosis to get the final diagnosis. The experimental results show that the ensemble of multiple heterogeneous component learners can enhance the generalization ability of the model, and the diagnostic accuracy is better than single classifier and the homogenous ensemble classifier. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： transformer DGA fault diagnosis random forest stacking algorithm

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Lithology identification technology based on the stacking fusion model

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INTERNATIONAL JOURNAL OF OIL GAS AND COAL TECHNOLOGY 2023年第4期34卷 337-358页

作者： Hu, Chong Deng, Rui Zhang, Yuanpeng Chen, Jie Li, Ming Dang, Lixia Zhou, Feng Shi, Huibing Lu, Hongyan Xiong, Kun Yangtze Univ 111 Daxue Rd Wuhan Hubei Peoples R China PetroChina Tuha Oilfield Co 1 Chuangye Rd Hami Xinjiang Peoples R China PetroChina Qinghai Oilfield Co 9 Kunlun Middle Rd Dunhuang Gansu Peoples R China

A lithology identification method based on stacking multi-model fusion was studied, which solved the problem of poor recognition performance of traditional single machine learning models. In the experiment, logging data underwent preprocessing using outlier and linear analysis. Nine data features were filtered to identify valid features. Classification and regression tree, K-nearest neighbour algorithm, random forest, and extreme gradient boosting were used as base models. Principal component analysis calculated the weights of each model and applied them to the light gradient boosting machine metamodel in the second layer, constructing a multi-layer ensemble learning model. The fusion model improved the F1-score by 1.63 percentage points compared to random forest. In the siltstone with the best average recognition performance, the improvement was 9.24 percentage points over the K-nearest neighbour algorithm. These results verify the higher accuracy and F1-score of the fusion model as compared to traditional single algorithms, demonstrating the effectiveness of the fusion model method.

关键词： lithology identification stacking algorithm principal component analysis fusion model

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stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirs

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SOFT COMPUTING 2022年第22期26卷 12271-12290页

作者： Khosravi, Khabat Safari, Mir Jafar Sadegh Khozani, Zohreh Sheikh Crookston, Brian Golkarian, Ali Ferdowsi Univ Mashhad Dept Watershed Management Engn Mashhad Razavi Khorasan Iran Florida Int Univ Dept Earth & Environm Miami FL 33199 USA Yasar Univ Dept Civil Engn Izmir Turkey Bauhaus Univ Weimar Inst Struct Mech D-99423 Weimar Germany Utah State Univ Dept Civil & Environm Engn Logan UT 84322 USA

Labyrinth weirs are utilized to transport a greater discharge during floods in contrast to conventional weirs due to their increased weir crest length. Nevertheless, due to the increased geometric complexity of labyrinth weirs, determination of accurate discharge coefficients and accordingly, head-discharge ratings are quite essential issues in practical application. Hence, as a first step the present study proposes the following eight standalone algorithms: decision table (DT), Kstar, least median square (LMS), M5 prime (M5P), M5 rule (M5R), pace regression (PR), random forest (RF) and sequential minimal optimization (SMO). Then, applying the stacking (ST) algorithm, these standalone models were hybridized to predict the discharge coefficient (C-d) for sharp-crested labyrinth weirs. Potential/effective variables were constructed in the form of several independent dimensionless parameters (i.e., theta, h/W, L/B, L/h, Froude number (Fr), B/W and L/W) to predict C-d as an output. The accuracy of the developed models was examined in terms of different statistical visually based and quantitative-based error measurement criteria. The results illustrate that h/W and B/W parameters have the highest and lowest effect on the C-d prediction, respectively. According to NSE, all developed algorithms provided accurate performances, while ST-Kstar had the highest prediction power.

关键词： Discharge coefficient Hybridization Labyrinth weir stacking algorithm Machine learning

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Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laser powder bed fusion via a stacking model

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Frontiers of Mechanical Engineering 2024年第4期19卷 29-43页

作者： Changjun HAN Fubao YAN Daolin YUAN Kai LI Yongqiang YANG Jiong ZHANG Di WANG School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou 510641China Department of Mechanical Engineering College of EngineeringCity University of Hong KongHong Kong 999077China

Determining appropriate process parameters in large-scale laser powder bed fusion(LPBF)additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during *** work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first *** method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base ***,the stacking model utilized artificial neural network(ANN),gradient boosting regressor,kernel ridge regression,and elastic net as base models,with the Lasso model serving as the *** optimization and cross-validation were utilized for model optimization and training based on a limited data set,resulting in higher predictive accuracy compared to traditional artificial neural network *** statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set,with a coefficient of determination value of 0.944,mean absolute percentage error of 2.51%,and root mean squared error of 27.64,surpassing that of the ANN *** statistical metrics demonstrate superiority over those obtained from the ANN *** results confirm that by integrating the base models,the stacking model exhibits superior predictive stability compared to individual base models alone,thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process.

关键词： machine learning laser powder bed fusion ensemble learning stacking algorithm additive manufacturing

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An improved stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 541卷 122272-122272页

作者： Jiang, Minqi Liu, Jiapeng Zhang, Lu Liu, Chunyu China Jiliang Univ Dept Financial Engn Hangzhou Peoples R China

Stock price index is an essential component of financial systems and indicates the economic performance in the national level. Even if a small improvement in its forecasting performance will be highly profitable and meaningful. This manuscript input technical features together with macroeconomic indicators into an improved stacking framework for predicting the direction of the stock price index in respect of the price prevailing some time earlier, if necessary, a month. Random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), which pertain to the tree-based algorithms, and recurrent neural networks (RNN), bidirectional RNN, RNN with long short-term memory (LSTM) and gated recurrent unit (GRU) layer, which pertain to the deep learning algorithms, are stacked as base classifiers in the first layer. Cross-validation method is then implemented to iteratively generate the input for the second level classifier in order to prevent overfitting. In the second layer, logistic regression, as well as its regularized version, are employed as meta-classifiers to identify the unique learning pattern of the base classifiers. Empirical results over three major U.S. stock indices indicate that our improved stacking method outperforms state-of-the-art ensemble learning algorithms and deep learning models, achieving a higher level of accuracy, F-score and AUC value. Besides, another contribution in our research paper is the design of a Lasso (least absolute shrinkage and selection operator) based meta-classifier that is capable of automatically weighting/selecting the optimal base learners for the forecasting task. Our findings provide an integrated stacking framework in the financial area. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Stock index prediction Tree-based ensemble models Deep learning stacking algorithm Information fusion

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