Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4943001 | Expert Systems with Applications | 2018 | 18 Pages |
Abstract
Based on the experimental results, it may be stated that feature selection improves the performance of MWE recognition by eliminating the noisy/non-effective features. Moreover, it is obvious that proposed feature selection method contributes to the overall MWE recognition system by reducing the measurement and storage requirements due to the lower number of features in classification, providing a faster and more-cost effective learning model.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Senem Kumova Metin,