Intelligent feature extraction and knowledge mining by multivariate analyses

• Published in: 2009 IEEE Symposium on Computational Intelligence and Data Mining pp. 33 - 39
• Main Authors: Yisong Chen, Hong Cui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2009
• Subjects: Analytical models; Data analysis; Data visualization; Decision making; Educational institutions; Feature extraction; Machine intelligence; Multidimensional systems; Support vector machine classification; Support vector machines
• ISBN: 9781424427659; 1424427657
• DOI: 10.1109/CIDM.2009.4938626

A new knowledge mining framework based on multivariate analyses is proposed to discover and simulate the school grading policy. The framework comprises three major steps. Firstly, factor analysis is adopted to separate the scores of several different subjects into grading-related ones and grading-unrelated ones. Secondly, 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.