طبقه‌بند همباشی ادراکی مبتنی بر منطق فازی توسعه یافته

نوع مقاله : علمی-پژوهشی

نویسندگان

1 قطب علمی رایانش نرم و پردازش هوشمند اطلاعات-گروه مهندسی کامپیوتر- دانشکده مهندسی- دانشگاه فردوسی مشهد

2 دانشکده برق و کامپیوتر- دانشگاه ارومیه

چکیده

پایگاه قوانین طبقه‌بند فازی همباشی (FAC)، مجموعه‌ای از قوانین فازی همباشی است که اغلب مبتنی بر داده‌های کمّی سیستم می‌باشد. درحالی‌که در دنیای واقعی- با پیچیدگی‌ها و عدم قطعیت‌های موجود- طبقه‌بندی، یک مسئله تصمیم‌گیری است که تحت تاًثیر شدید دانش، تجربه، و دیدگاه شخصی افراد می‌باشد. در این مقاله، ساختار کلی ف-طبقه‌بند فازی همباشی(f-FAC) را در چارچوب منطق فازی توسعه‌یافته معرفی می‌نماییم که بیش از پیش به شیوه تفکر و استنتاج آدمی نزدیک می‌باشد. در ساختار پیشنهادی، دانش و تجربه انسانی در قالب مفهوم اعتبار فازی در هر دو مرحله تشکیل پایگاه قوانین و استنتاج طبقه بندهای فازی همباشی لحاظ شده است. در این طبقه‌بند، اعتبار مشخصه‌ها و قوانین با تلفیق نظرکارشناسان براساس هوش‌جمعی و با استفاده از محاسبات ادراکی تعیین می‌گردد. به منظور ارزیابی روش پیشنهادی، f-FARC-HD به عنوان توسعه‌ای از طبقه‌بند FARC-HD پیاده‌سازی شده و با تعدادی از طبقه‌بندهای دیگر- فازی همباشی و غیرفازی همباشی- مقایسه می‌شود. همچنین، کارآیی دو طبقه‌بند f-FARC-HD و FARC-HD در سطوح مختلف اغتشاش بررسی می‌گردد. آزمایش‌ها بر روی یک مجموعه داده واقعی ازاطلاعات بیماران بخش سوختگی بیمارستان‌های اهواز اجرا شده است. نتایج نشان می‌دهد که با در نظر گرفتن مفهوم اعتبار در f-FARC-HD، طبقه‌بندی کارا با پیچیدگی بسیار کم‌تر بدست می‌آید که حساسیت آن نسبت به تغییرات اغتشاش کمتر از FARC-HD می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Perceptual Associative Classifier based on Extended Fuzzy Logic

نویسندگان [English]

  • M. Kadkhoda 1
  • M.-R Akbarzadeh-T 1
  • F. Sabahi 2
1 Center of Excellence on Soft Computing and Intelligent Information Processing, Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2 Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran
چکیده [English]

Rule base of a Fuzzy Associative Classifier (FAC) is a collection of fuzzy associative rules that are often based on the system's quantitative data. However, due to the real-world complexities and uncertainties, classification in many practical circumstances remains a matter of art of decision-making that is strongly influenced by the knowledge, experience, and personal perspective of individuals. In this paper, we introduce the f-associative fuzzy classifier (f-FAC) in the framework of Extended Fuzzy Logic (FLe), which is more closely related to the way of thinking and reasoning of human beings. In the proposed structure, human knowledge and experience are considered by fuzzy validity concept in both phases of construction and deduction of FACs. In this classifier, the validity of the items and rules is determined by integrating the opinion of experts on the basis of wisdom of crowds and using perceptual computing. To evaluate the proposed approach, a real dataset of burn patients in Ahwaz are considered. f-FARC-HD is then implemented as an extension of FARC-HD associative classifier and is compared with the other approaches (associative classifier and non-associative classifier). Also, f-FARC-HD and FARC-HD are compared in different levels of noise. Results indicate that considering the concept of validity in the proposed extended approach, f-FARC-HD, leads to comparable accuracy, but at a considerably less complexity. Also, f-FARC-HD is less sensitive against noise.

کلیدواژه‌ها [English]

  • Validity
  • Extended Fuzzy Logic
  • Fuzzy Associative Classifier
  • Uncertainty
  • Perceptual Computing

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