کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
403778 677350 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data
ترجمه فارسی عنوان
دسته بندی کننده انطباقی نمونه های اولیه نزدیکترین K با آموزش ازدحام با انتخاب ویژگی اتوماتیک برای داده های فاصله‌ای
کلمات کلیدی
بهینه سازی ازدحام؛ فاصله وزن؛ یادگیری نمونه اولیه؛ انتخاب ویژگی؛ تجزیه و تحلیل داده های نمادین؛ داده های فاصله
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 80, August 2016, Pages 19–33
نویسندگان
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