کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4969499 1449973 2018 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
چکیده انگلیسی
While standing as one of the most widely considered and successful supervised classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor efficiency due to being an instance-based method. In this sense, Approximated Similarity Search (ASS) stands as a possible alternative to improve those efficiency issues at the expense of typically lowering the performance of the classifier. In this paper we take as initial point an ASS strategy based on clustering. We then improve its performance by solving issues related to instances located close to the cluster boundaries by enlarging their size and considering the use of Deep Neural Networks for learning a suitable representation for the classification task at issue. Results using a collection of eight different datasets show that the combined use of these two strategies entails a significant improvement in the accuracy performance, with a considerable reduction in the number of distances needed to classify a sample in comparison to the basic kNN rule.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 74, February 2018, Pages 531-543
نویسندگان
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