پژوهش های نوین در تصمیم گیری

مدل ترکیبی یادگیری ماشین مبتنی بر رأی‌گیری وزنی برای طبقه‌بندی هوشمند ارزش مشتریان بانکی

نوع مقاله : مقاله پژوهشی

نویسندگان

امیرمحمد خانی 1
احمد جعفرنژاد 2
آرمان رضاسلطانی 1

1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

2 استاد، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

چکیده
در فضای رقابتی صنعت بانکداری، شناسایی و طبقه‌بندی دقیق ارزش مشتریان، نقشی کلیدی در طراحی استراتژی‌های بازاریابی هدفمند، تخصیص بهینه منابع و افزایش سودآوری دارند. این پژوهش، یک مدل ترکیبی پیشرفته مبتنی بر یادگیری ماشین را با استفاده از تکنیک رأی‌گیری وزنی طراحی و پیاده‌سازی می کند که هدف آن طبقه‌بندی هوشمند مشتریان بانکی بر‌اساس میزان ارزش آنهاست. شش الگوریتم قدرتمند یادگیری ماشین شامل Random Forest، Gradient Boosting، XGBoost، LightGBM، CatBoost و Extra Trees به‌عنوان مدل‌های پایه انتخاب و با دو روش رأی‌گیری سخت و نرم ترکیب شده اند. وزن مشارکت هر الگوریتم در فرآیند رأی‌گیری به‌صورت بهینه با الگوریتم Optuna تنظیم گردیده تا دقت و تعادل مدل به حداکثر برسد. همچنین، روش ADASYN برای مقابله با مشکل عدم‌توازن کلاس‌ها در داده‌های واقعی بانکی به کار رفته و مهم‌ترین ویژگی‌های مؤثر در پیش‌بینی با بهره‌گیری از الگوریتم Random Forest شناسایی گردیده‌اند. عملکرد مدل پیشنهادی با استفاده از چهار معیار ارزیابی صحت، دقت، بازخوانی و امتیاز F1 و در مقایسه با ۱۶ الگوریتم کلاسیک و مدرن یادگیری ماشین تحلیل شده است. نتایج نشان می دهد که مدل رأی‌گیری سخت با صحت 0.9426، دقت 0.9756 و بازخوانی 0.9112، و مدل رأی‌گیری نرم با دقت 0.9771 و امتیاز F1 معادل 0.9422، عملکردی برتر از سایر الگوریتم‌ها مانند SVM، KNN و Logistic Regression داشته‌ و تعادلی مؤثر میان معیارهای ارزیابی برقرار کرده‌اند. این مدل ترکیبی، با دقت بالا، مقاومت در برابر داده‌های نامتوازن، و انعطاف‌پذیری در انتخاب ویژگی‌ها، الگویی کاربردی و قابل اتکا برای بانک‌ها و مؤسسات مالی جهت تحلیل ارزش مشتریان فراهم می‌آورد.

کلیدواژه‌ها

ارزش مشتریان بانکی
رأی‌گیری وزنی
یادگیری ماشین
طبقه‌بندی هوشمند

عنوان مقاله English

A hybrid machine learning model based on weighted voting for intelligent classification of bank customer value

نویسندگان English

Amir Mohammad Khani 1
Ahmad Jafarnjad 2
Arman Rezasoltani, 1
1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
2 Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
چکیده English

Accurately determining and categorizing customer value is crucial for targeted marketing campaigns, efficient resource allocation, and increasing profitability in the highly competitive and ever-changing banking sector. This study proposes a machine leanring-based sophisticated hybrid model using a weighted voting technique to intelligently classify banking customers based on their value.Both hard and soft voting techniques were used to implement the combinations of six potent machine learning algorithms chosen as base models: Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, and Extra Trees. The Optuna algorithm was utilized to optimally adjust the contribution weight of each algorithm in the voting process and optimize the accuracy and balance of the model. Additionally, the ADASYN method and Random Forest algorithm were used to address the problem of class imbalance in real-world banking data and identify the most influential features for prediction, respectively. Four metrics—accuracy, precision, recall, and F1-score—were used to assess the suggested model's performance, and it was contrasted with 16 traditional and contemporary machine learning algorithms. the results showed that the hard voting model outperformed other algorithms like SVM, KNN, and Logistic Regression and effectively balancing evaluation metrics, obtaining an accuracy of 0.9426, precision of 0.9756, and recall of 0.9112, while the soft voting model recorded a precision of 0.9771 and an F1-score of 0.9422. This hybrid model provides banks and other financial institutions with a useful and trustworthy framework for analyzing customer value because of its high accuracy, resilience to unbalanced data, and feature selection flexibility.

کلیدواژه‌ها English

Bank customer value
weighted voting
machine learning
intelligent classification
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