Distinguishing and Clustering Breast Cancer According to Hierarchical Structures Based on Chaotic Multispecies Particle Swarm Optimization
محل انتشار: دوازدهمین کنفرانس ملی سیستم های هوشمند ایران
سال انتشار: 1392
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 802
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شناسه ملی سند علمی:
ICS12_186
تاریخ نمایه سازی: 11 مرداد 1393
چکیده مقاله:
Breast Cancer Diagnosis and Prognosis are two medical applications pose a great challenge to the researchers. The use of machine learning and data mining techniques hasrevolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast tumors are divided in to two types;malignant and benign. In this paper we propose how todistinguish the type of breast cancer by creating a Fuzzy system (FS). To detect the type of breast censer we use achaotic hierarchical cluster-based multispecies particle swarm optimization (CHCMSPSO) to optimization a FS indeed. Theobjective of this paper is to learn Takagi-Sugeno-Kang (TSK) type fuzzy rules with high accuracy. In addition to this, we will introduce chaos into the HCMSPSO in order to furtherenhance its global search ability. In the paper, eleven chaotic maps are used in the intelligent diagnosis system. The accuracyrate of distinguishing between benign and malignant censer is above 90 percent. However, among the chaotic maps, theSinusoidal chaotic map provides us with the accuracy rate 99percent because it coordinates with the problems conditions. This simulation is performed on UCI-Breast Censer data-base.
کلیدواژه ها:
نویسندگان
Maryam Yassi
Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Alireza Yassi
Department of Information technology, Asia Pacific University, kualalumpur, Malaysia
Mehdi Yaghoobi
Department of Artificial Intelligence, Mashhad Branch, Islamic Azad University, Mashhad, Iran