دوره 20، شماره 4 - ( 12-1402 )
جلد 20 شماره 4 صفحات 34-23 |
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Hamidzadeh J, Rashidi Mahmoodi M A, Moradi M. Online Learning for Imbalanced Data Streams with Concept Drift by Belief Theory and Chaotic Function. JSDP 2024; 20 (4) : 2
URL: http://jsdp.rcisp.ac.ir/article-1-1246-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1246-fa.html
حمیدزاده جواد، رشیدی محمودی محمدعلی، مرادی منا. یادگیری برخط داده‌های جریانی نامتوازن دارای رانش مفهوم به‌وسیله نظریه باور و تابع آشوب. پردازش علائم و داده‌ها. 1402; 20 (4) :23-34
دانشگاه سجاد
چکیده: (388 مشاهده)
خصوصیات داده‌های جریانی در گذر زمان ناپایدار بوده و توزیع طبقات متحمل تغییرات می‌شوند؛ بنابراین مدل‌های یادگیری اغلب نیاز به تطبیق با رانش مفاهیم دارند. در این مقاله، با هدف حل دو چالش نبود توازن میان طبقات مشاهده‌شده و وقوع رانش مفهوم، طبقه‌بند داده‌های جریانی نامتوازن دارای رانش مفهوم ارائه شده است. روش پیشنهادی سعی در حذف داده‌های جریانی مرزی و نوفه‌ای با کمک خوشه‌بندی دارد. داده‌ها با کمک تابع باور وزندهی شده است و با درنظرگرفتن برچسب داده‌ها، نمونهافزایی در نواحی کمتراکم طبقه کمینه و با رویکرد آشوبی انجام می‌گیرد. سپس، با تعریف حدود آستانه، رانش مفهوم پدید آمده از نوع تدریجی و افزایشی شناسایی می‌شود. پیش‌بینی برچسب به‌وسیله طبقه‌بند ترکیبی و رأیگیری وزن‌دار بیشینه انجام می‌پذیرد. عملکرد روش پیشنهادی بر روی مجموعهداده‌های پایگاه داده UCI به‌وسیله روش LOO ارزیابی و با طبقه‌بندهای مرز دانش مقایسه شدهاست. نتایج آزمایش‌ها نشان‌دهنده برتری روش پیشنهادی از نظر معیارهای ارزیابی است.
شماره‌ی مقاله: 2
نوع مطالعه: پژوهشي |
موضوع مقاله:
مقالات پردازش داده‌های رقمی
دریافت: 1400/4/10 | پذیرش: 1402/4/14 | انتشار: 1403/2/6 | انتشار الکترونیک: 1403/2/6
دریافت: 1400/4/10 | پذیرش: 1402/4/14 | انتشار: 1403/2/6 | انتشار الکترونیک: 1403/2/6
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