Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 30, Issue 4, Pages 658-669Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2007.70730
Keywords
pattern classification; classification error rate; discriminant functions; polynomials and machine learning
Funding
- National Research Foundation of Korea [R11-2002-105-09001-0] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This paper presents a deterministic solution to an approximated classification-error-based objective function. In the formulation, we propose a quadratic approximation as the function for achieving smooth error counting. The solution is subsequently found to be related to the weighted least-squares, whereby a robust tuning process can be incorporated. The tuning traverses between the least-squares estimate and the approximated total-error-rate estimate to cater to various situations of unbalanced attribute distributions. By adopting a linear parametric classifier model, the proposed classification-error-based learning formulation is empirically shown to be superior to that using the original least-squares-error cost function. Finally, it will be seen that the performance of the proposed formulation is comparable to other classification-error-based and state-of-the-art classifiers without sacrificing the computational simplicity.
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