Publication Type : Conference Proceedings
Publisher : Procedia Computer Science
Source : Procedia Computer Science, Volume 167, p.313-322 (2020)
Url : https://www.sciencedirect.com/science/article/pii/S1877050920306943
Keywords : classification, Feature selection, Fractal dimension, Random Sampling, RSFS, SFS
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2020
Abstract : The lung sounds produced by a human convey valuable information about the health of the respiratory system, and these signals are complex in nature. In this paper, a study was conducted to find the importance of feature selection from these signals for the purpose of classification. Feature selection is performed using two different approaches: RSFS and SFS. The experiment was conducted on a dataset of 85 samples using the (SVM, KNN, and Naïve Bayes) classifiers. The computational results obtained are promising, and the proposed feature selection techniques show better performances in terms of Precision, Recall, and F-Measures.
Cite this Research Publication : Dr. Don S., “Random Subset Feature Selection and Classification of Lung Sound”, Procedia Computer Science, vol. 167. pp. 313-322, 2020.