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Discovering hidden knowledge in data classification via multivariate analysis
Author(s) -
Chen Yisong,
Ip Horace H.S.,
Li Sheng,
Wang Guoping
Publication year - 2010
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00508.x
Subject(s) - computer science , dimensionality reduction , multivariate statistics , data mining , grading (engineering) , visualization , multidimensional scaling , support vector machine , artificial intelligence , curse of dimensionality , machine learning , pattern recognition (psychology) , civil engineering , engineering
Abstract: 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.