Improving Fuzzy-Rough Quick Reduct for Feature Selection

سال انتشار: 1390
محل انتشار: نوزدهمین کنفرانس مهندسی برق ایران
کد COI اختصاصی: ICEE19_025
زبان مقاله: انگلیسی
تعداد مشاهده: 1080

نویسندگان

Department of Computer Engineering, Shahid Bahonar University of Kerman

چکیده

Feature selection is a process of selecting subset of features which are highly correlated with classification outcome and lowly depends on other features. Rough set has been successfully applied to nominal datasets for feature selection. Since datasets might have real-valued data, Fuzzy set theory has been combined with Rough set for feature selection of continuous datasets. Fuzzy Rough Set Feature Selection (FRFS) is computationally prohibitive. Many researchers proposed new methods to diminish the computation of FRFS. A new method based on Fuzzy Lower Approximation-Based Feature Selection is proposed which selects smaller subset of features, makes better classification accuracy and run faster than the base method, especially on big datasets. This is performed using a threshold based stopping criterion which prevents adding more features in QuickReduct algorithm. Experimental results on UCI datasets confirm the performance and effectiveness of our proposed method

کلیدواژه ها

Feature selection, Threshold, Fuzzy Lower Approximation, UCI datasets

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.