A new formation of supervised dimensionality reduction method for moving vehicle classification

Analyzing a large number of features set for the classification process entails cost and complexity. To reduce this burden, dimensionality reduction has been applied to the extracted set of features as a preprocessing step. Among dimensionality reduction algorithms, many methods fail to handle high-dimensional data and they increase information loss and are sensitive to outliers. Therefore, this research proposes a new supervised dimensionality reduction method developed using an improved formation of linear discriminant analysis with diagonal eigenvalues (LDA-DE) that simultaneously preserves the information and addresses the issues of the classification process. The proposed framework focuses on reducing the dimension of extracted features set by computing the scattered matrices from the class labels and the diagonal eigenvalue matrix. Methods to eliminate duplicate rows and columns, to avoid feature overwriting, and to remove outliers are included in the newly developed LDA-DE method. The new LDA-DE method implemented with a fuzzy random forest classifier is tested on two datasets—MIO-TCD and BIT-Vehicle—to classify the moving vehicles. The performance of our LDA-DE method is compared with five state-of-the-art dimensionality reduction methods. The experimental confusion matrix results show that the LDA-DE method generates the reduced feature vector of the objects to a maximum extent. Further, the newly developed LDA-DE method achieves the best reduction results with optimal performance parameter values (lowest mean and standard deviation and highest f-measure and accuracy) and minimal data processing time than the state-of-the-art methods, promising its application for a fast and effective dimensionality reduction for moving vehicle classification.