Ferroelectric Memristive Networks for Dimensionality Reduction: A Process for Effectively Classifying Cancer Datasets

Abstract

In this work, a copper-doped (5%) zinc oxide (Cu:ZnO) ferroelectric materials-based memristor model was realized and it was employed to develop principal component analysis (PCA), a data dimension reduction technique. The developed PCA was utilized to efficaciously classify breast cancer datasets, which are considered as complex and big volumes of data. It was found that the controllable memristance variations were analogous to the weight modulations in the implemented neural network-based learning systems. Sanger’s rule was utilized to achieve unsupervised online learning in order to generate the principal components. On one side, the developed memristor-based PCA network was found to be effective to isolate distinct breast cancer classes with a high classification accuracy of 97.77% and the error in the classification of malignant cases as benign of 0.529%, a significantly low value. On the other side, the power dissipation was found to be 0.27 µW, which suggests the proposed memristive network is suitable for low-power applications. Further, a comparison was established with other existing non-memristor and non-PCA-based data classification systems. Furthermore, the devised less complex equations to implement PCA on this memristive crossbar array could be employed to implement any neural network algorithm.