ماشین یادگیری مفرط عمیق: رویکرد ترکیبی یادگیری افزایشی برای طبقه‌بندی داده‌های جریانی
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشگاه سجاد مشهد،دانشکده مهندسی کامپیوتر و فناوری اطلاعات
2 - دانشگاه سمنان،دانشکده مهندسی برق و کامپیوتر
کلید واژه: داده‌های جریانی, رانش مفهوم, ماشین یادگیری مفرط, یادگیری افزایشی,
چکیده مقاله :
داده‌های جریانی متشکل از داده‌هایی است که به ترتیب و با سرعت و حجم زیاد به سیستم وارد می‌شوند. توزیع این داده‌ها ناپایدار بوده و در طول زمان ممکن است تغییر کنند. با توجه به اهمیت این نوع داده‌ها در حوزه‌هایی مهم نظیر اینترنت اشیا، تسریع عملکرد و افزایش توان عملیاتی تحلیل داده‌های بزرگ جریانی به عنوان موضوعی مهم، مورد توجه محققین است. در روش پیشنهادی، از مفهوم یادگیری ترکیبی برخط در مدل بهبودیافته ماشین یادگیر مفرط به منظور طبقه‌بندی داده‌های جریانی استفاده شده است. به دلیل استفاده از رویکرد افزایشی، در هر لحظه تنها یک بلوک داده بدون نیاز به دسترسی به داده‌های پیشین یاد گرفته می‌شود. همچنین با بهره‌گیری از رویکرد آدابوست، وزن‌دهی به طبقه‌بندی‌کننده‌های پایه و تصمیم‌گیری در مورد حفظ و یا حذف آنها بر اساس کیفیت پیش‌بینی‌ها انجام می‌شود. مزیت دیگر روش پیشنهادی، بهره‌گیری از رویکرد مبتنی بر صحت طبقه‌بندی کننده جهت شناسایی رانش مفهوم است که منجر به تسهیل انطباق مدل و افزایش کارایی آن می‌شود. آزمایش‌ها بر روی مجموعه داده‌های استاندارد انجام گردید و روش پیشنهادی به طور میانگین با کسب 90/0% خاص‌بودن، 69/0% حساسیت و 87/0% صحت توانست اختلاف معناداری با دو روش رقیب داشته باشد.
Streaming data refers to data that is continuously generated in the form of fast streams with high volumes. This kind of data often runs into evolving environments where a change may affect the data distribution. Because of a wide range of real-world applications of data streams, performance improvement of streaming analytics has become a hot topic for researchers. The proposed method integrates online ensemble learning into extreme machine learning to improve the data stream classification performance. The proposed incremental method does not need to access the samples of previous blocks. Also, regarding the AdaBoost approach, it can react to concept drift by the component weighting mechanism and component update mechanism. The proposed method can adapt to the changes, and its performance is leveraged to retain high-accurate classifiers. The experiments have been done on benchmark datasets. The proposed method can achieve 0.90% average specificity, 0.69% average sensitivity, and 0.87% average accuracy, indicating its superiority compared to two competing methods.
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