Design and Performance Evaluation of a Committee Machine for Gas Identification

Abstract

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 %.