Random Subset Feature Selection and Classification of Lung Sound

被引：4

作者：

Don, S. ^{[1
]}

机构：

[1] VIT, Sch Comp Sci & Engn, TIFAC CORE Automot Infotron, Vellore, Tamil Nadu, India

来源：

INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE | 2020年 / 167卷

关键词：

Feature Selection; Fractal Dimension; RSFS; SFS; Random Sampling; Classification; SPECTROGRAM;

D O I：

10.1016/j.procs.2020.03.228

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 Naive 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. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：313 / 322

页数：10

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