Income prediction via support vector machine

Abstract

Principal component analysis and support vector machine methods are employed to generate and evaluate income prediction data based on the Current Population Survey provided by the U.S. Census Bureau. A detailed statistical study targeted for relevant feature selection is found to increase efficiency and even improve classification accuracy. A systematic study is performed on the influence of this statistical narrowing on the grid parameter search, training time, accuracy, and number of support vectors. Accuracy values as high as 84%, when compared against a test population, are obtained with a reduced set of parameters while the computational time is reduced by 60%. Tailoring computational methods around specific real data sets is critical in designing powerful algorithms.

DOI: 10.1109/ICMLA.2004.1383506

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Cite this paper

@inproceedings{Lazar2004IncomePV, title={Income prediction via support vector machine}, author={Alina Lazar}, booktitle={ICMLA}, year={2004} }