A Support Vector Machine-Based Genetic AlgorithmMethod for Gas Classification

Support vector machine (SVM) now attracts increasing attention in gas classification due to its high performance towards small samples and nonlinearity problems of the dataset. Previously, the probable mismatch between the dataset and the training parameters determined by trial and error or grid search may hinder the exploration of the best result. In this paper, we propose a novel approach to estimate the most suitable training parameters, based on the inbreeding prevention of genetic algorithm (GA) by assigning the training model parameters of SVM as its chromosome. Treating the k-fold cross validation of SVM training as the objective function, our new method makes the population on the whole evolve towards the values that are more appropriate for the dataset. The inbreeding prevention mechanism (IPM) can protect the population from converging over-rapidly before reaching the optimum value. Compared with the standard SVM, the proposed method has greatly improved the prediction accuracy in both training data and testing data.