Dealing With Redundant Features and Inconsistent Training Data in Electronic Nose: A Rough Set Based Approach-Reference-Cited by-同舟云学术

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Dealing With Redundant Features and Inconsistent Training Data in Electronic Nose: A Rough Set Based Approach

Published:2014-03 Issue:3 Volume:14 Page:758-767
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Bag Anil Kumar,Tudu Bipan,Bhattacharyya Nabarun,Bandyopadhyay Rajib

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/6704724/06636022.pdf?arnumber=6636022

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recognition of Oil Content for Oil Shale by Electronic Nose Based on Transient and Steady-State Fusion Feature Extraction Method;IEEE Sensors Journal;2023-02-15

2. A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose;Applied Sciences;2023-02-11

3. A Novel Low-Cost Hand-Held Tea Flavor Estimation System;IEEE Transactions on Industrial Electronics;2018-06

4. Advances of electronic nose and its application in fresh foods: A review;Critical Reviews in Food Science and Nutrition;2017-09-06

5. Data Mining and Modelling of Charpy Impact Energy for Alloy Steels Using Fuzzy Rough Sets;IFAC-PapersOnLine;2017-07

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