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بهبود کارایی شبکه عصبی کانولووشنال با استفاده از تابع ضرر وزندار افزایشی برای مقابله با نامتوازنی دسته‌ای | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 2، دوره 11، شماره 4 - شماره پیاپی 44، اسفند 1402، صفحه 17-34 اصل مقاله (1.94 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| نسیبه محمودی1؛ حسین شیرازی* 2؛ محمد فخردانش3؛ کوروش داداش تبار احمدی3 | ||
| 1دانشجوی دکتری، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر | ||
| 2دانشیار، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر | ||
| 3استادیار، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر | ||
| تاریخ دریافت: 08 شهریور 1402، تاریخ بازنگری: 21 آبان 1402، تاریخ پذیرش: 22 آذر 1402 | ||
| 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 | ||
| نامتوازنی دسته‌ای؛ یادگیری عمیق؛ تابع ضرر؛ آنتروپی متقابل | ||
| موضوعات | ||
| فناوری های نوین دفاع الکترونیک و سایبری | ||
| 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 Mahmoodi1؛ Hossein Shirazi2؛ Mohammad Fakhredanesh3؛ Kourosh Dadashtabar Ahmadi3 | ||
| 1PhD student, Faculty of Electrical and Computer Engineering, Malik Ashtar University of Technology | ||
| 2Associate Professor, Faculty of Electrical and Computer Engineering, Malik Ashtar University of Technology | ||
| 3Assistant 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 | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 174 تعداد دریافت فایل اصل مقاله: 155 |
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