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IDCOST: روشی برای افزایش سرویس معیار داده‌ها با امتیازدهی داده‌های نامتعادل اعتباری با استفاده از SVM کاربردی | ||
| مدل سازی در مهندسی | ||
| دوره 23، شماره ویژه 81، تیر 1404، صفحه 1-18 اصل مقاله (1.16 M) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2025.31252.2493 | ||
| نویسندگان | ||
| آرش قربان نیادلاور* ؛ صدف السادات ضیاء | ||
| گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه پیام نور، تهران، ایران | ||
| تاریخ دریافت: 22 تیر 1402، تاریخ بازنگری: 11 مرداد 1403، تاریخ پذیرش: 12 دی 1403 | ||
| چکیده | ||
| مسائل داده‌های اعتباری یکی از مسائل مهم در داده‌کاوی SVM کاربردی می‌باشد که داده‌های نامتعادل نسبت به داده‌های متعادل نقش مهم‌تری دارد. لذا ما روشی برای افزایش معیارها با امتیازدهی داده‌های نامتعادل اعتباری با استفاده از SVM کاربردی را ارائه دادیم. به‌همین منظور تکنیک امتیاز‌بندی و همچنین انتخاب سرخوشه مناسب در طبقه‌بندی داده‌ها می‌تواند نقش مهمی در اجرا و دقت برنامه داشته باشد که زمان اجرای برنامه بهینه شده است. با مطالعه موردی بر روی روش‌های شبکه باور عمیق و ماشین بردار پشتیبان، داده‌های نامتعادل اعتباری مورد توجه قرارنگرفته است. با ادغام نمودن انتخاب ویژگی شاخص، همچنین فاصله و اصل همسایگی تابع صلاحیتی را ارائه دادیم که نسبت به روش‌های مورد مطالعه قرار گرفته، از نظر کمی و کیفی می‌تواند معیار مجموعه داده‌ها و کشف تقلب کارت اعتباری و همچنین مجموعه داده‌های پیش‌فرض کارت اعتباری و حساسیت تشخیص روش پیشنهادی و نمونه‌های غیر پیش‌فرض با استفاده از روش IDCOST انجام شده است. که در آن با ایجاد نمونه‌برداری مناسب مجموعه داده‌ها توانستیم افزونگی داده و داده‌های تکراری را نسبت به روش‌های مورد مطالعه کاهش دهیم و همچنین تعادل مجموعه داده‌های انتخاب ویژگی را در روش IDCOST با شاخص معتبری توانستیم، انجام دهیم، تا با این روش افزایش کارایی را در بر داشته‌باشد. | ||
| کلیدواژه‌ها | ||
| معیار داده‌ها؛ داده‌های نامتعادل اعتباری؛ سرخوشه؛ امتیازدهی؛ تعادل بار | ||
| عنوان مقاله [English] | ||
| IDCOST: A Method for Increasing Data Criterion Service by Scoring Credit Imbalanced Data Using Applied SVM | ||
| نویسندگان [English] | ||
| Arash Ghorbannia Delavar؛ Sadaf Sadat Ziya | ||
| Department of Computer Engineering and Information Technology, Payam Noor University, Tehran, Iran | ||
| چکیده [English] | ||
| Unbalanced credit data can pose significant challenges in applied data mining. To address this, we propose a method that utilizes a scoring technique and support vector machine (SVM) to enhance data criterion service. Our approach integrates index feature selection and IDCOST method, which reduces data redundancy and balances feature selection data sets with a valid index. We also use feature selection and kernel modification to improve accuracy while reducing computational complexity and execution time. Our proposed method can detect credit card fraud and credit card default data sets with higher sensitivity than other methods. It presents a promising solution for tackling credit data issues in applied SVM data mining and has the potential to improve data analysis accuracy and reduce computational complexity in various fields. The IDCOST method is presented in pre-processing, training, validation, and testing stages. We use detector threshold clustering in the pre-processing stage, sensitivity and feature validation on the models in the training stage, and score each sample in the test dataset in the testing stage. The proposed method's accuracy is optimized by selecting an appropriate cluster head in data classification and employing a scoring technique. In conclusion, our proposed method is an effective solution for tackling credit data issues in applied SVM data mining. By integrating index feature selection, IDCOST method, feature selection, and kernel modification, we can accurately detect credit card fraud and credit card default data sets while reducing data redundancy and computational complexity. | ||
| کلیدواژه‌ها [English] | ||
| Data criterion, Credit imbalanced data, Cluster head, Scoring, Load balancing | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 76 تعداد دریافت فایل اصل مقاله: 28 |
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