Feature Selection on High Dimensional Histogram Data to Improve Vehicle Components´ Life Length Prediction

Abstract: Feature selection plays an important role in life length prediction. A well selectedfeature subset can reduce the complexity of predictive models and help understand the mechanism of the ageing process. This thesis intends to investigate the potential  of applying feature selection and machine learning on vehicles' operational data to predict the life length of diesel particulate filters. Filter-based feature selection methods with Pearson correlation coefficient, mutual information and analysis of variance are experimented and compared with a wrapper-based method, recursive feature elimination. The selected subsets are evaluated by linear regression, support vector machine, and multilayer perceptron. The results show that filters and wrappers are both able to significantly reduce the input feature sizes while keeping the model performance. In particular, by recursive feature elimination, 5 variables are selected from 130 with classification accuracy over 90%.