A Novel Classification Algorithm Based on Multidimensional F1 Fuzzy Transform and PCA Feature Extraction

The bi‐dimensional F1‐Transform was applied in image analysis to improve the performances of the F‐transform method; however, due to its high computational complexity, the multidimensional F1‐transform cannot be used in data analysis problems, especially in the presence of a large number of features. In this research, we proposed a new classification method based on the multidimensional F1‐Transform in which the Principal Component Analysis technique is applied to reduce the dataset size. We test our method on various well‐known classification datasets, showing that it improves the performances of the F‐transform classification method and of other well‐known classification algorithms; furthermore, the execution times of the F1‐Transform classification method is similar to the ones obtained executing F‐transform and other classification algorithms.