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A gradient boosting classifier for purchase intention prediction of online shoppers

HELIYON 2023年第4期9卷 e15163页

作者： Abdullah-All-Tanvir Khandokar, Iftakhar Ali Islam, A. K. M. Muzahidul Islam, Salekul Shatabda, Swakkhar United Int Univ Dept Comp Sci & Engn Plot-2 Dhaka 1212 Bangladesh

Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers for product suggestions, offer discount and for many other interventions. Several work has already been done using session log for analyzing customer behavior whether he performs a purchase on the product or not. In most cases, it is difficult to find out and make a list of customers and offer them discount when their session ends. In this paper, we propose a customer's purchase intention prediction model where e-shoppers can detect customer's purpose earlier. First, we apply feature selection technique to select best features. Then the extracted features are fed to train supervised learning models. Several classifiers like support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and XGBoost classifiers have been applied along with oversampling method for balancing the dataset. The experiments were performed on a standard benchmark dataset. Experimental results show that XGBoost classifier with feature selection techniques and oversampling method has the significantly higher area under ROC curve (auROC) score and are under precision-recall curve (auPR) score which are 0.937 and 0.754 respectively. On the other hand accuracy achieved by XGBoost and Decision tree are significantly improved and they are 90.65% and 90.54% respectively. Overall performance of the gradient boosting method is significantly improved compared to other classifiers and state-of-the-art methods. In addition to this, a method for explainable analysis on the problem was outlined.

关键词： gradient boosting classifier Imbalanced dataset Feature selection Online shopper's purchase intention Real time prediction

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Computational Analysis of Diabetes Kidney Diseases Using Machine Learning

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2025年

作者： Chandra, Ganesh Tiwari, Namita Mahor, Urmila Pal, Parashuram Yadav, Vikash Mishra, Deepak Kumar Madhav Inst Technol & Sci Deemed Univ Dept Comp Sci & Engn Gwalior Madhya Pradesh India CSJMU Kanpur Sch Basic Sci Dept Math Kanpur Uttar Pradesh India Oriental Coll Technol Bhopal Madhya Pradesh India Trinity Inst Technol & Res Bhopal Madhya Pradesh India Govt Polytech Bighapur Unnao Dept Tech Educ Manpur Uttar Pradesh India Univ Delhi Coll Vocat Studies Delhi 110007 India

The increasing complexity of healthcare, coupled with an ageing population, poses significant challenges for decision-making in healthcare delivery. Implementing smart decision support systems can indeed alleviate some of these challenges by providing clinicians with timely and personalized insights. These systems can leverage vast amounts of patient data, advanced analytics, and predictive modeling to offer clinicians a comprehensive view of individual patient needs and potential ***, researchers and doctors need a faster solution for various diseases in health care. So they started to use the Machine Learning (ML) algorithms for better solution. ML is a sub field of Artificial Intelligence (AI) that provides a useful tool for data analysis, automatic process and others for healthcare system. The use of ML is increasing continuously in healthcare system due to its learning *** this paper the following algorithms are used for the diagnosis of Diabetes and Kidney Disease such as: gradient boosting classifier (GBC), Random Forest classifier (RFC), Extra Trees classifier (ETC), Support Vector classifier (SVC) and Multilayer Perceptron (MNP) Neural Network, In our model, gradient boosting classifier is used with repeated cross validation to develop our system for better results. The experiment analysis performed for both unbalanced and balanced dataset. The accuracy achieved in case of unbalanced and balanced datasets for GBC, ETC, RFC SVC, MLP & DTC are 75.7 & 92.2, 75.7 & 90.1, 74.4 & 80.0, 62.5 & 66.4, 58.3 & 63.0 and 59.4 & 74.5 respectively. On comparing these results, we found that GBC results are better than other algorithms.

关键词： Random forest classifier gradient boosting classifier machine learning support vector classifier

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Geostatistical analyses empowered with gradient boosting and extra trees classifier algorithms in the prediction of groundwater quality and geology-lithology attributes over YSR district, India

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2023年第4期16卷 333-369页

作者： Kumar, Mogaraju Jagadish IUCN Commiss Ecosyst Management Rajampet 516115 Andhra Prades India

Machine learning classifiers are integrated with the geostatistical analyses through interpolation techniques to predict groundwater quality and geology-lithology. Ordinary kriging is used to obtain the optimal interpolation model using RMSSE values. The data extracted from the surface maps were passed onto ML algorithms, resulting in prediction accuracies of 99% for groundwater quality and 96% in predicting the geology-lithology features. There was certain overfitting in the prediction and it was probably due to several classes of geology-lithology variables and limited data available for analysis. The interpolation methods might affect the model performance by producing overfitting and underfitting results. It is to report that the gradient boosting classifier yielded relatively high prediction accuracies in predicting groundwater quality when two classes were used. The extra trees classifier returned better results in predicting geology-lithology features when multiple classes were used in this study.

关键词： machine learning geostatistics groundwater quality gradient boosting classifier extra trees classifier India

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Realistic Data Delays and Alternative Inactivity Definitions in Telecom Churn: Investigating Concept Drift Using a Sliding-Window Approach

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APPLIED SCIENCES-BASEL 2025年第3期15卷

作者： Bugajev, Andrej Kriauziene, Rima Chadysas, Viktoras Vilnius Gediminas Tech Univ Fac Fundamental Sci Dept Math Modelling Sauletekio Ave 11 LT-10223 Vilnius Lithuania Vilnius Gediminas Tech Univ Fac Fundamental Sci Dept Math Stat Sauletekio Ave 11 LT-10223 Vilnius Lithuania

Predicting customer churn is essential for telecommunications companies to maintain profitability. However, training models on historical models leads to performance degradation when they are applied to future conditions-a phenomenon known as concept drift. We employ a sliding-window approach that separates the training and testing time windows, creating a future-based "true test". Using unique real data, we show that a CatBoost classifier model trained on older data can remain relevant when new, unseen intervals are used. A key innovation of our work is the use of 40-day "partial churn" labels;a model trained on these labels accurately predicts 90-day churn by simply adjusting the decision threshold. Out of the six modeled scenarios, in the main realistic scenario, CatBoost retained an accuracy above 0.798 and an F1 of near 0.704, reflecting its robustness even under real-world delays and potential drift. Overall, our findings emphasize that models do not necessarily "expire" with time;rather, their performance varies according to when they are tested. This research underscores the importance of a truly future-based evaluation (instead of artificial splits) and offers practical guidance for earlier churn detection when facing real-world data delays.

关键词： concept drift customer churn prediction gradient boosting classifier machine learning sliding-window approach telecom churn CatBoost

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Predicting Accurate Employee Performance: An Evaluation of Regression Models

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Procedia Computer Science 2025年 259卷 433-442页

作者： Garima Vijh Nisha Sharma Shivam Tiwari Surbhi Vijh Ameet Sao Sharda University Greater Noida Uttar Pradesh ASET Amity University Noida Uttar Pradesh RICS Amity University Noida Uttar Pradesh

Accurately predicting employee performance is critical for organizations seeking to enhance productivity and decision-making. The conventional methodologies for machine learning such as linear regression (LR) and Decision Tree analysis have been utilized in prior research; but they frequently struggle to effectively capture the intricacies of employee behaviour. In this study, the various regression techniques, such as a random decision forest approach, gradient optimization, and neural network modelling, to predict employee performance are utilized. gradient boosting classifier achieved the lowest Mean Squared Error (MSE) of 0.0211, while KNN and Neural Networks performed poorly with MSE values of 0.0383 and 0.0417, respectively. This research demonstrates the potential of advanced machine learning models, particularly gradient boosting, for improving predictive accuracy in employee performance assessments.

关键词： Employee Performance Prediction Regression Models gradient boosting classifier Machine Learning (ML) Mean Squared Error (MSE)

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Dementia classification using MR imaging and clinical data with voting based machine learning models

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第18期81卷 25971-25992页

作者： Bharati, Subrato Podder, Prajoy Dang Ngoc Hoang Thanh Prasath, V. B. Surya Bangladesh Univ Engn & Technol Inst Informat & Commun Technol Dhaka Bangladesh Univ Econ Ho Chi Minh City Coll Technol & Design Dept Informat Technol Ho Chi Minh City Vietnam Univ Cincinnati Dept Elect Engn & Comp Sci Cincinnati OH 45221 USA Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA Univ Cincinnati Dept Pediat Coll Med Cincinnati OH 45257 USA Univ Cincinnati Coll Med Dept Biomed Informat Cincinnati OH 45267 USA

Dementia is one of the leading causes of severe cognitive decline, it induces memory loss and impairs the daily life of millions of people worldwide. In this work, we consider the classification of dementia using magnetic resonance (MR) imaging and clinical data with machine learning models. We adapt univariate feature selection in the MR data pre-processing step as a filter-based feature selection. Bagged decision trees are also implemented to estimate the important features for achieving good classification accuracy. Several ensemble learning-based machine learning approaches, namely gradient boosting (GB), extreme gradient boost (XGB), voting-based, and random forest (RF) classifiers, are considered for the diagnosis of dementia. Moreover, we propose voting-based classifiers that train on an ensemble of numerous basic machine learning models, such as the extra trees classifier, RF, GB, and XGB. The implementation of a voting-based approach is one of the important contributions, and the performance of different classifiers are evaluated in terms of precision, accuracy, recall, and F1 score. Moreover, the receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) are used as metrics for comparing these classifiers. Experimental results show that the voting-based classifiers often perform better compared to the RF, GB, and XGB in terms of precision, recall, and accuracy, thereby indicating the promise of differentiating dementia from imaging and clinical data.

关键词： Dementia classification MR imaging Random forest XGB classifier Voting classifiers gradient boosting classifier Feature selection

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Roman Urdu News Headline Classification Empowered with Machine Learning

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Computers, Materials & Continua 2020年第11期65卷 1221-1236页

作者： Rizwan Ali Naqvi Muhammad Adnan Khan Nauman Malik Shazia Saqib Tahir Alyas Dildar Hussain Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computational Sciences Korea Institute for Advanced StudySeoul02455Korea

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak *** from the subcontinent may speak the same Urdu language but they might be using different scripts for *** communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information *** Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the *** is due to the numerous complexities involved in the processing of Roman Urdu *** complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured *** have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be *** author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different *** preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the *** applying above mentioned different machine learnin

关键词： Roman urdu news headline classification long short term memory recurrent neural network logistic regression multinomial naïve Bayes random forest k neighbor gradient boosting classifier

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Fake News Classification Methodology With Enhanced BERT

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IEEE ACCESS 2024年 12卷 164491-164502页

作者： Oad, Ammar Farooq, Muhammad Hamza Zafar, Amna Akram, Beenish Ayesha Zhou, Ruogu Dong, Feng Shaoyang Univ Fac Informat Engn Shaoyang 422000 Peoples R China Univ Engn & Technol Lahore UET Lahore Natl Ctr Artificial Intelligence KICS Lahore Pakistan Univ Engn & Technol Lahore Dept Comp Sci Lahore 54890 Pakistan Univ Engn & Technol Dept Comp Engn Lahore 54890 Pakistan Hunan Vocat Coll Commerce Changsha 410205 Peoples R China

News serves as a vital source of information for staying updated on various aspects of life worldwide. However, massive volume of information available on social media platforms makes it challenging to extract meaningful insights. Additionally, dispersion of false information has grown broader, often serving specific agendas. In this work, we present a novel fake news classification methodology based on an enhanced BERT deep learning model which is trained on self-developed PolitiTweet datasets along with benchmarked Buzzfeed dataset. The PolitiTweet dataset is augmented to solve class imbalance problem and improve data diversity to capture regional language nuances, cultural references that help in more accurate detection of fake news. For this purpose, We enhance BERTbase model by adding 3 additional layers namely Linear Layer, Dropout Layer, Activation Layer and fine tuned the model to train enhanced BERT classifier. The fine tuned BERT model trained on augmented dataset is capable of capturing patterns and nuances within the data, giving better classification results. Subsequently, the enhanced BERT model is evaluated against BERTbase model for further elaboration on the generalisibility and effective performance of the fine tuned model for real-world cases. The enhanced BERT model achieved an accuracy of 85% on Buzzfeed and 98% on PolitiTweet. In comparison the baseline BERT models achieved an average accuracy of 81% and 88%, respectively. The proposed Enhanced BERT model uses a mix of pre-training strategies with fine-tuning techniques to achieve better performance. The developed research data is available online at: https://***/datasets/ameerhamza123/pak-tweets.

关键词： Fake news Accuracy Social networking (online) Support vector machines Long short term memory Encoding Bidirectional control Cultural differences Classification algorithms Transformers Bidirectional encoder representations from transformers (BERT) natural language processing transformers fake news classification gradient boosting classifier machine learning (ML) deep learning (DL) large language model (LLM)

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Machine learning strengthened prediction of tracheal, bronchus, and lung cancer deaths due to air pollution

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第45期30卷 100539-100551页

作者： Mogaraju, Jagadish Kumar Int Union Conservat Nat Commiss Ecosyst Managemen Agroecosyst Specialist Grp New Delhi 110001 India

This work pointed out the use of machine learning tools to predict the effect of CO, O-3, CH4, and CO2 on TBL (tracheal, bronchus, and lung cancer) deaths from 1990 to 2019. In this study, data from 203 countries/locations were used. We used evaluation metrics like accuracy, area under curve (AUC), recall, precision, and Matthews correlation coefficient (MCC) to determine the prediction efficiency of the models. The models that yielded accuracy between 89 and 90 were selected in this study. The essential features in the prediction process were extracted, and it was found that CO influenced the prediction process. Extra trees classifier, random forest classifier, gradient boosting classifier, and light gradient boosting machine were selected from 14 other classifiers based on the accuracy metric. The best-performing models, according to our benchmark standards, are the extra trees classifier (90.83%), random forest classifier (89.17%), gradient boosting classifier (89.17%), and light gradient boosting machine (89.17). We conclude that machine learning models can be used in predicting mortality, i.e., the number of deaths, and could assist us in predicting the role of air pollutants on TBL deaths globally.

关键词： Machine learning Extra trees classifier Radom forest classifier gradient boosting classifier TBL cancer Mortality

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Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia

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INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY 2019年第7期34卷 941-949页

作者： Facal, David Valladares-Rodriguez, Sonia Lojo-Seoane, Cristina Pereiro, Arturo X. Anido-Rifon, Luis Juncos-Rabadan, Onesimo Univ Santiago De Compostela Dept Dev & Educ Psychol Santiago De Compostela Spain Univ Vigo Sch Telecommun Engn Vigo Spain

Objectives The overall aim of the present study was to explore the role of cognitive reserve (CR) in the conversion from mild cognitive impairment (MCI) to dementia. We used traditional and machine learning (ML) techniques to compare converter and nonconverter participants. We also discuss the predictive value of CR proxies in relation to the ML model performance. Methods In total, 169 participants completed the longitudinal study. Participants were divided into a control group and three MCI subgroups, according to the Petersen criteria for diagnosis. Information about the participants was compared using nine ML classification techniques. Seven relevant performance metrics were computed in order to evaluate the accuracy of prediction regarding converter and nonconverter participants. Results ML algorithms applied to socio-demographic, basic health, and CR proxy data enabled prediction of conversion to dementia. The best performing models were the gradient boosting classifier (accuracy (ACC) = 0.93;F1 = 0.86, and Cohen kappa = 0.82) and random forest classifier (ACC = 0.92;F1 = 0.79, and Cohen kappa = 0.71). Use of ML techniques corroborated the protective role of CR as a mediator of conversion to dementia, whereby participants with more years of education and higher vocabulary scores survived longer without developing dementia. Conclusions We used ML approaches to explore the role of CR in conversion from MCI to dementia. The findings indicate the potential value of ML algorithms for detecting risk of conversion to dementia in cognitive aging and CR studies. Further research is required to develop an ML-based procedure that can be used to make robust predictions.

关键词： cognitive reserve dementia diagnostic transitions educational level gradient boosting classifier machine learning mild cognitive impairment random forest classifier supervised learning vocabulary

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