کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
536330 870500 2015 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maximum distance minimization for feature weighting
ترجمه فارسی عنوان
حداکثر کاهش فاصله برای وزن گذاری ویژگی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• A simple linear programming based feature weighting algorithm is presented.
• The results are independent of the initial scaling of the input dimensions.
• It yields competitive results on UCI datasets.
• The algorithm automatically performs a dimensionality reduction.

We present a new feature weighting method to improve k-Nearest-Neighbor (k-NN) classification. The proposed method minimizes the largest distance between equally labeled data tuples, while retaining a minimum distance between data tuples of different classes, with the goal to group equally labeled data together. It can be implemented as a simple linear program, and in contrast to other feature weighting methods, it does not depend on the initial scaling of the data dimensions. Two versions, a hard and a soft one, are evaluated on real-world datasets from the UCI repository. In particular the soft version compares very well with competing methods. Furthermore, an evaluation is done on challenging gene expression data sets, where the method shows its ability to automatically reduce the dimensionality of the data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 52, 15 January 2015, Pages 48–52
نویسندگان
, ,