کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405085 677479 2014 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Feature selection via neighborhood multi-granulation fusion
ترجمه فارسی عنوان
انتخاب ویژگی از طریق همجوشی چند گرانولازی محله
کلمات کلیدی
محاسبات گرانول، انتخاب ویژگی، چند دانه مجموعه های خشن همجوار، نفوذ گرانولیتی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Feature selection is an important data preprocessing technique, and has been widely studied in data mining, machine learning, and granular computing. However, very little research has considered a multi-granulation perspective. In this paper, we present a new feature selection method that selects distinguishing features by fusing neighborhood multi-granulation. We first use neighborhood rough sets as an effective granular computing tool, and analyze the influence of the granularity of neighborhood information. Then, we obtain all feature rank lists based on the significance of features in different granularities. Finally, we obtain a new feature selection algorithm by fusing all individual feature rank lists. Experimental results show that the proposed method can effectively select a discriminative feature subset, and performs as well as or better than other popular feature selection algorithms in terms of classification performance.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 67, September 2014, Pages 162–168
نویسندگان
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