Jaccard Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices and Its Application to Diagnosis of Parkinson’s Disease
محل انتشار: نهمین کنگره مشترک سیستم های فازی و هوشمند ایران
سال انتشار: 1400
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 155
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شناسه ملی سند علمی:
FJCFIS09_060
تاریخ نمایه سازی: 10 اردیبهشت 1401
چکیده مقاله:
This paper introduces a new similarity measure of fuzzy parameterized fuzzy soft matrices (fpfs-matrices), i.e., Jaccard pseudo-similarity of fpfs-matrices. We then provide its basic properties. Afterwards, we apply it to the diagnosis of Parkinson’s Disease (PD), improving a machine learning (ML) approach. Next, we compare our approach with the well-known ML approaches, such as Naïve Bayes, 𝒌-Nearest Neighbor (𝒌NN), Support Vector Machine (SVM), Fuzzy 𝒌NN, Decision Trees (DT), Boosted Trees (BT), Adaptive Boosting Tree (AdaBoost), and Random Forest (RF) in terms of accuracy, specificity, and sensitivity. The results manifest that the proposed approach makes a more accuratediagnosis of PD than the others.
کلیدواژه ها:
نویسندگان
Samet Memiş
Department of Computer Engineering Faculty of Engineering and Natural Sciences İstanbul Rumeli University İstanbul, Turkey
Serdar Enginoğlu
Department of Mathematics Faculty of Arts and Sciences Çanakkale Onsekiz Mart University Çanakkale, Turkey
Uğur Erkan
Department of Computer Engineering Faculty of Engineering Karamanoğlu Mehmetbey University Karaman, Turkey