A novel classifier ensemble for recognition of multiple indoor air contaminants by an electronic nose

• This paper studies a novel classifier ensemble for multi-classification problems.
• Nonlinear feature extraction method is studied using kernel trick.
• An improved fusion strategy was used to integrate decisions from base classifiers.
• The proposed ensemble classifier was compared with standard majority voting method.

This paper presents a novel multiple classifiers system called as improved support vector machine ensemble (ISVMEN) which solves a multi-class recognition problem in electronic nose (E-nose) and aims to improve the accuracy and robustness of classification. The contributions of this paper are presented in two aspects: first, in order to improve the accuracy of base classifiers, kernel principal component analysis (KPCA) method is used for nonlinear feature extraction of E-nose data; second, in the process of establishing classifiers ensemble, a new fusion approach which conducts an effective base classifier weighted method is proposed. Experimental results show that the average classification accuracy has been improved from less than 86% to 92.58% compared with that of base classifiers. Besides, the proposed fusion method is also superior to MV fusion method (majority voting) which has 90.1% of classification accuracy. Especially, the proposed ISVMEN can obtain the best discrimination accuracy for C7H8, CO and NH3, almost 100% classification accuracy was obtained using our method. Therefore, it is easy to come to the conclusion that, in average, the proposed method is better significantly than other methods in classification and generalization performance.