Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

A fundamental problem in machine learning is identifying the most representative subset of features from which we can construct a predictive model for a classification task. This paper aims to present a validation study of dimensionality reduction effect on the classification accuracy of mammographic images. The studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the classification rate is about 95%. It was also found that the classification rate increases with the size of the reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun index. The measurement of these indices confirms that the optimal value of reduced space dimension is d=60.