Discovering hidden knowledge in data classification via multivariate analysis
A new classification algorithm based on multivariate analysis is proposed to discover and simulate the grading policy on school transcript data sets. The framework comprises three major steps. First, factor analysis is adopted to separate the scores of several different subjects into grading-related ones and grading-unrelated ones. Second, multidimensional scaling is employed for dimensionality reduction to facilitate subsequent data visualization and interpretation. Finally, a support vector machine is trained to classify the filtered data into different grades. This work provides an attractive framework for intelligent data analysis and decision making. It also exhibits the advantages of high classification accuracy and supports intuitive data interpretation.
multivariate analysis factor analysis multidimensional scaling dimensionality reduction data visualization support vector machine pattern classification feature selection