بهبود کارایی شبکه عصبی کانولووشنال با استفاده از تابع ضرر وزن‌دار افزایشی برای مقابله با نامتوازنی دسته‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر

2 دانشیار، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر

3 استادیار، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر

چکیده

باتوجه ‌به اینکه بیشتر مسائل دنیای واقعی از ﻗﺒﯿﻞ تشخیص تقلب، شناسایی خطا،  ﺗﺸﺨﯿﺺ ﻧﺎﻫﻨﺠﺎری، ﺗﺸﺨﯿﺺ ﭘﺰشکی و تشخیص بدافزار نامتوازن هستند، دسته­بندی داده­ﻫﺎ در مسائل ﻧﺎمتوازن ﺑﻪ ﻋﻨﻮان یکی از ﭼﺎﻟﺶ­ﻫﺎی اصلی در حوزة داده­ﮐﺎوی، ﻣﻮرد ﺗﻮﺟﻪ ﺑﺴﻴﺎری از ﻣﺤﻘﻘﺎن و ﭘﮋوﻫﺶﮔﺮان ﻗﺮارﮔﺮﻓﺘﻪ اﺳﺖ. در یادگیری نامتوازن، ﻣﻌﻤﻮﻻ ﺗﻌﺪاد ﻧﻤﻮﻧﻪ­ﻫﺎی یکی از دسته­ﻫﺎ ﺧﯿلی ﺑﯿﺸﺘﺮ از ﻧﻤﻮﻧﻪﻫﺎی دسته دیگر اﺳﺖ و یا هزینه دسته­بندی اشتباه در دودسته متفاوت است. شبکه­های عصبی کانولووشنال به‌رغم موفقیت­های چشمگیری که در دسته­بندی داده­ها دارند، در مسائل نامتوازن با مشکل مواجه می­شوند چرا که آنها به‌صورت پیش­فرض، ﺗﻮزﯾﻊ دسته­ﻫﺎ را متوازن و هزینه دسته­بندی را مساوی در ﻧﻈﺮ ﮔﺮﻓﺘﻪ می­گیرند، ازاین‌رو در دسته­بندی نامتوازن، نمی­توان به ﻧﺘﺎﯾﺞ قابل‌قبولی دست‌یافت؛ زﯾﺮا شبکه ﺑﻪ ﺳﻤﺖ ﻧﻤﻮﻧﻪ­ﻫﺎی آﻣﻮزشی دسته ﺑﺰرگ­ﺗﺮ ﻣﺘﻤﺎﯾﻞ می‌شود ﮐﻪ اﯾﻦ ﻣﻮﺿﻮع ﺳﺒﺐ اﻓﺰاﯾﺶ ﺗﻌﺪاد ﺧﻄﺎﻫﺎ در تشخیص نمونه­ﻫﺎی ﻣﺜﺒﺖ می­ﺷﻮد.  یکی از راهکارهای کم­هزینه برای غلبه بر نامتوازنی داده­ها در شبکه­های  عصبی کانولوشنال استفاده از تابع ضرر به نفع دسته اقلیت است، در این مقاله تابع ضرری جدیدی معرفی شده است که به‌صورت تدریجی و با پیشرفت آموزش، اهمیت دسته اقلیت را افزایش می­دهد تا در انتهای آموزش به مقدار مشخص شده برسد و از اهمیت داده­های دسته اکثریت بکاهد، این امر باعث می­شود تا هم بتوانیم از قدرت آموزشی همه داده­ها استفاده کنیم و هم از غلبه داده­های دسته اکثریت جلوگیری کنیم. نتایج آزمایش روی سه مجموعه‌دادة مصنوعی، تشخیص فعالیت­های انسان و Cifar-10، همگرایی و کارایی روش پیشنهادی را نشان می­دهند، روش پیشنهادی با روش­های آدابوست مبتنی بر درخت تصمیم، شبکه کانولوشنال مبتنی بر آنتروپی متقابل و آنتروپی متقابل وزن­دار، روش SMOTE و روش CNN تجمعی مقایسه شده است. به ترتیب با کسب دقت 6/94، 92/92 و 23/69 در سه مجموعه­داده  (Cifar-10 با نرخ نامتوازنی 5 درصد) توانست از دیگر روش­ها پیشی بگیرد و دقت در مجموعه­داده مصنوعی نسبت به روش سنتی آدابوست مبتنی درخت تصمیم، 72/17 بالاتر است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Improving the Performance of the Convolutional Neural Network Using Incremental Weight loss Function to Deal with Class Imbalanced Data

نویسندگان [English]

  • Nasibeh Mahmoodi 1
  • Hossein Shirazi 2
  • Mohammad Fakhredanesh 3
  • Kourosh Dadashtabar Ahmadi 3
1 PhD student, Faculty of Electrical and Computer Engineering, Malik Ashtar University of Technology
2 Associate Professor, Faculty of Electrical and Computer Engineering, Malik Ashtar University of Technology
3 Assistant Professor, Faculty of Electrical and Computer Engineering, Malik Ashtar University of Technology
چکیده [English]

Class-imbalanced datasets are common in many real-world domains, such as health, banking, and security. Machine learning researchers have recently focused on the classification of such datasets, where the costs of different types of misclassifications are unequal, the classes have different prior probabilities, or both. The performance of most standard classifier learning algorithms is significantly affected by class imbalance, where the algorithms are often biased toward the majority class instances despite recent advances in deep learning. However, there is very little empirical work on deep learning with class imbalance.To address this issue, we propose an incremental weighted cross entropy loss function. The proposed method involves gradually increasing the weight of the minority class as the training progresses, until it reaches the specified amount at the end of the training. Through experiments, we demonstrate the convergence and efficiency of the proposed method. The results of experiments on three datasets, including artificial datasets, human activity recognition dataset, and CIFAR-10, demonstrate the convergence and performance of the proposed method. The proposed method is compared with decision tree-based AdaBoost, Cross Entropy-based convolutional neural network, weighted Cross Entropy -based CNN, SMOTE method, and ensemble CNNs method. With accuracy gains of 94.6%, 92.92%, and 69.23% on the three datasets (CIFAR-10 with 5% imbalance rate), the proposed method outperformed the other methods. Additionally, the accuracy on the artificial dataset was 17.77% higher than the traditional decision tree-based AdaBoost method.
 

کلیدواژه‌ها [English]

  • Class-imbalanced dataset
  • Convolutional Neural Network
  • Loss function
  • Cross-entropy

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https://creativecommons.org/licenses/by/4.0/

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پدافند الکترونیکی و سایبری
دوره 11، شماره 4 - شماره پیاپی 44
(شماره پیاپی 44، فصلنامه زمستان)
اسفند 1402
صفحه 17-34

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سابقه مقاله

  • تاریخ دریافت: 08 شهریور 1402
  • تاریخ بازنگری: 21 آبان 1402
  • تاریخ پذیرش: 22 آذر 1402
  • تاریخ انتشار: 28 دی 1402

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