Recognition of different datasets using PCA, LDA, and various classifiers

Bayesian, k-nearest neighbor, and Parzen window classifiers along with PCA and LDA methods, are effective tools in machine learning. In this work, a hybrid method is formed by the above mentioned methods. The aim is to achieve a successful, fast, and low computational classification. Performance of the new method is evaluated on five various kinds of datasets, from UCI machine learning datasets, including Breast Cancer, Iris, Glass, Yeast, and Wine. The experimental results indicate the superior performance of the proposed method in comparison with the previous works.