An Effective Gas Sensor Array Optimization Method Based on Random Forest

Abstract

The quality of gas sensor array is directly related to the performance of the electronic nose, which makes the optimization of sensor array a key issue in the study of electronic noses. A new sensor array optimization method is proposed based on Random Forest by using the Gini importance as the new measure of sensor contributions. An optimal sensor array of two sensors is built up targeting to classify CO, CH4 and their mixtures from an initial array composed of six sensors based on the method. Recognition results with the selected and other sensors by Random Forest, Back Propagation Neural Network and Support Vector Machine prove the effectiveness of the proposed array optimization algorithm.