Principal component analysis is commonly used as a pre-step before
employing a classifier to avoid the negative effect of the dimensionality and multicollinearity.
The performance of a classifier is severely affected by the deviations from the linearity of the
data structure and noisy samples. In this paper, we propose a new classification system that
overcomes the drawback of these crucial problems, simultaneously. Our proposal is relying
on the kernel principal component analysis with a proper parameter selection approach with
data complexity measures. According to the empirical results, F1, T2 and T3 in AUC, T3 in
GMEAN and T2 and T3 in MCC performed better than classical and other complexity
measures. Comparison of classifiers showed that Radial SVM performs better in AUC, and
KNN performs better in GMEAN and MCC using KPCA with complexity measures. As a
result, our proposed system produces better results in various classification algorithms with
respect to classical approach.