Recursive DBPSO for computationally efficient electronic nose system

Feature-rank-code-based classifiers have been proposed recently in order to reduce the complexity for electronic nose system (ENS). The performance of these classifiers is degraded when the discriminatory information of gases lie in the actual feature values not in the ranks of features. To overcome the problem, a gas identification system based on a simple distance measure in combination with different type of features is proposed. In order to improve the computational cost of the existing ENS, a novel combination of optimum subset of features is proposed. To achieve the aim of low computational cost, a large feature vector is recursively reduced to a small number of features without compromising the classification accuracy of the system. Discrete binary particle swarm optimization, a metaheuristic for global search is used in a recursive setup to select the optimum subset of features for classification. Euclidian distance is used as a similarity measure for identification of different industrial gases. The proposed system is tested for the identification of 13 different industrial gases, namely, C₃H₈, Cl₂, CO, CO₂, SO₂ , NO₂, NH₃, C₂H₄O, C₃H₆O, C₂H₄, C₂H₆O, C₇H₈, and CH₄. These gases are contained in three different data sets; two among these data sets are acquired experimentally in a laboratory setup, while one of these data sets is taken from the University of California at Irvine machine learning repository. Results reveal that the computational cost and the memory requirement of an ENS can be significantly reduced by combining different type of features. An average classification accuracy of 98.17% is achieved by the proposed system with an average 69.04% reduction in memory requirement using only three to five features.