Impact on Classification Process Generated by Corrupted Features

The topic of this study is the testing of the robustness of machine learning (ML) and neural network (NN) models with a new idea based on corrupted data. Typically, ML and NN classifiers are trained on real feature data; however, a portion of the features may be false, with noise, or incorrect. The undesired content was analyzed in eight experiments with false data, six with feature noise, and six with label noise. These tests were all conducted on the public Breast Cancer Wisconsin Dataset (BCWD). Throughout this, the false and noise data were gradually corrupted in a random way, generating new data and replacing raw features that belonged to the BCWD. Artificial Intelligence (AI) should be properly selected while categorizing different diseases using medical data. The Pearson correlation coefficient (PCC) applied between features monitored their correlation in each experiment, and a correlation matrix between both true and false features was used. Four machine learning (ML) algorithms—Random Forest (RF), XGBClassifier (XGB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were used, as well as for the analysis of important features (IF) and the binary classification. The study was completed using three deep neural networks—a simple Deep Neural Network (DNN), a Convolutional Neural Network (CNN), and a Transformer Neural Network (TNN). In the context of a binary classification, the accuracy, F1-score, Area Under the Curve (AUC), and Matthews correlation coefficient (MCC) metrics of the performance of classification in malignant versus benign breast cancer (BC) was computed. The results demonstrated the robustness of some methods and the sensitivity of other machine learning algorithms in the context of corrupted data, computational cost, and hyperparameters optimization.