TY - CHAP
T1 - Design and Performance Evaluation of a Committee Machine for Gas Identification
AU - Akbar, Muhammad Ali
AU - Djelouat, Hamza
AU - Ait Si Ali, Amine
AU - Amira, Abbes
AU - Bensaali, Faycal
AU - Benammar, Mohieddine
AU - Bermak, Amine
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - 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 %.
AB - 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 %.
KW - Binary Decision Tree (BDT)
KW - Classifiers
KW - Committe Machine (CM)
KW - Extended Nearest Neighbours (ENN)
KW - K-Nearest Neighbours (KNN)
UR - http://www.scopus.com/inward/record.url?scp=85062907941&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56991-8_69
DO - 10.1007/978-3-319-56991-8_69
M3 - Chapter
AN - SCOPUS:85062907941
T3 - Lecture Notes in Networks and Systems
SP - 936
EP - 945
BT - Lecture Notes in Networks and Systems
PB - Springer
ER -