Design and Performance Evaluation of a Committee Machine for Gas Identification

Muhammad Ali Akbar, Hamza Djelouat, Amine Ait Si Ali, Abbes Amira, Faycal Bensaali, Mohieddine Benammar, Amine Bermak

Research output: Chapter in Book/Report/Conference proceedingChapter


Selecting the best classifier plays a significant role in the current electronic nose systems that can be deployed for gas applications. For this purpose, this paper presents an empirical study on the performance of three different classifiers, namely, binary decision tree (BDT), K-nearest neighbours (KNN) and extended nearest neighbours (ENN) on gas identification. It has been observed that with BDT and ENN a maximum classification accuracy of up to 96.4 % and 96.7 % can be obtained, respectively, whereas in the case of KNN up to 97.0 % accuracy can be achieved. In addition to the individual classifiers, a committee machine (CM) based on the three classifiers has been designed, with and without feedback mechanism to determine the improvement gained by combining these classifiers. The performance attained by the CM with feedback is 97.44 % and it is slightly better than the one without feedback, that is 97.2 %.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Number of pages10
Publication statusPublished - 1 Jan 2018

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389



  • Binary Decision Tree (BDT)
  • Classifiers
  • Committe Machine (CM)
  • Extended Nearest Neighbours (ENN)
  • K-Nearest Neighbours (KNN)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Control and Systems Engineering

Cite this

Akbar, M. A., Djelouat, H., Ait Si Ali, A., Amira, A., Bensaali, F., Benammar, M., & Bermak, A. (2018). Design and Performance Evaluation of a Committee Machine for Gas Identification. In Lecture Notes in Networks and Systems (pp. 936-945). (Lecture Notes in Networks and Systems; Vol. 16). Springer.