XGBoost-Based E-Commerce Customer Loss Prediction

Gan, Lin

doi:10.1155/2022/1858300

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Digital industry research is only a branch field in China, and there is little research on the actual progress of development indicators. CII will be used in this study, but the progress of ECI is slow [15].…”

Section: Introductionmentioning

confidence: 99%

Application of a Fuzzy Information Analysis and Evaluation Method in the Development of Regional Rural e-Commerce

Wang¹

2022

Advances in Multimedia

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As a new form of e-commerce, digital industry will become a tool to enhance regional competitiveness and win the allocation of resources, and its advanced degree is the standard to measure economic strength. In the process of development, countries and regions need to master their development status, so a set of digital industry evaluation index system is necessary to help various fields monitor the development status of e-commerce industry, aiming at providing more targeted services. Therefore, the establishment of regional digital industry progress index is conducive to evaluating and improving the development level of e-commerce. Because of the unbalanced development among different regions and the diversity and fuzziness of evaluation indexes, how to evaluate the development degree of cross-border electronic commerce in a region has become the main problem at present. In this paper, the evaluation index of regional promotion degree in cross-border electronic commerce is constructed, and on this basis, the intuitionistic fuzzy multiattribute group decision-making method based on induction is used to evaluate the promotion degree of regional cross-border electronic commerce. By analyzing the actual situation of cross-border Internet e-commerce comprehensive evaluation cases in various regions of Zhejiang Province, it is proved that this method is really effective. In view of this, this paper constructs an integrated multiangle monitoring index system of regional digital industry progress index, puts forward a subjective and objective method based on the principle of the minimum distance between the evaluated object and the ideal point, and combines the regional digital industry progress index with the comprehensive index method. The measurement model is used to measure the progress of digital industry in this region from June 2019 to June 2020, as an empirical object. The empirical results will show that this road is easy, and at the same time, we can fully grasp the progress of digital technology in a region.

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Section: Introductionmentioning

confidence: 99%

Application of a Fuzzy Information Analysis and Evaluation Method in the Development of Regional Rural e-Commerce

Wang¹

2022

Advances in Multimedia

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“…To establish a predictive model, the eleven most common machine learning models were trained using the final selected clinical factors. These machine models include SVM ( 13 ), KNN ( 14 ), RandomForest ( 15 ), ExtraTrees ( 16 ), XGBoost ( 17 ), LightGBM ( 18 ), NaiveBayes ( 19 ), AdaBoost ( 20 ), GradientBoosting ( 21 ), LR ( 22 ), MLP ( 23 ).…”

Section: Methodsmentioning

confidence: 99%

A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques

Fang,

Zheng,

Lin

et al. 2024

Front. Endocrinol.

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PurposeThe main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analysis of chest CT scans. An additional aim is to combine these techniques with bone turnover markers to identify and screen for osteoporosis in patients.MethodA total of 488 patients who had undergone chest CT and bone turnover marker testing, and had known bone mineral density, were included in this study. ITK-SNAP software was used to delineate regions of interest, while radiomics features were extracted using Python. Multiple 2D and 3D deep learning models were trained to identify these regions of interest. The effectiveness of these techniques in screening for osteoporosis in patients was compared.ResultClinical models based on gender, age, and β-cross achieved an accuracy of 0.698 and an AUC of 0.665. Radiomics models, which utilized 14 selected radiomics features, achieved a maximum accuracy of 0.750 and an AUC of 0.739. The test group yielded promising results: the 2D Deep Learning model achieved an accuracy of 0.812 and an AUC of 0.855, while the 3D Deep Learning model performed even better with an accuracy of 0.854 and an AUC of 0.906. Similarly, the 2D Transfer Learning model achieved an accuracy of 0.854 and an AUC of 0.880, whereas the 3D Transfer Learning model exhibited an accuracy of 0.740 and an AUC of 0.737. Overall, the application of 3D deep learning and 2D transfer learning techniques on chest CT scans showed excellent screening performance in the context of osteoporosis.ConclusionBone turnover markers may not be necessary for osteoporosis screening, as 3D deep learning and 2D transfer learning techniques utilizing chest CT scans proved to be equally effective alternatives.

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“…where n 0 = ∑ I ( x i ∈ o ),

, and

. (B) The XGBoost algorithm can generate a second-order Taylor expansion of the utilized loss function and obtain the optimal solution for the regular term outside the loss function ( 50 , 51 ). The larger the weight of a feature and the more times it is selected by the boosted tree, the more important the feature is considered to be ( 52 , 53 ).…”

Section: Methodsmentioning

confidence: 99%

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Chen

et al. 2023

Front. Neurol.

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BackgroundEarly stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction.MethodsThe research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method.ResultsAmong the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies.ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

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XGBoost-Based E-Commerce Customer Loss Prediction

Cited by 7 publications

References 24 publications

Application of a Fuzzy Information Analysis and Evaluation Method in the Development of Regional Rural e-Commerce

Application of a Fuzzy Information Analysis and Evaluation Method in the Development of Regional Rural e-Commerce

A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

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