مشخصات پژوهش

عنوان	Extracting Most Relevant Moments by Segment Based Hybrid Feature Selection Scheme for Classifying Radio Galaxy Images
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Radio Galaxy, Classification, Feature subset selection
چکیده	One of the significant concerns in radio galaxy image classification is how to deal with highdimensional feature space. Hence, in this paper, we design the segment-based hybrid feature selection scheme (SHFSS) via coupling information theory (filter phase) and machine learning algorithms (wrapper phase). Experimental results show that the selected features bring a higher accuracy than the original dimensions of the dataset for labeling samples.
پژوهشگران	سید علیرضا بشیری موسوی (نفر اول)، محسن جواهریان (نفر دوم)، محمد صادقی (نفر سوم)، حلیمه السادات میر آقایی جعفری (نفر چهارم)