Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling
Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar
Abstract
Training a machine learning algorithm from a class-imbalanced dataset is an inherently challenging task. The task becomes more challenging when compounded by high dimensionality (a high number of features). Feature extraction is a data reduction process that transforms features into linear or non-linear combinations of the original features, resulting in a smaller and richer set of attributes. Data sampling is a popular approach for addressing class imbalance. In this paper, our proposed method requires the implementation of feature extraction before data sampling, based on the idea that richer high-level features facilitate more efficient sampling and hence, produce better classification results. We use principal component analysis (PCA) and convolutional autoencoder (CAE) as the feature extraction techniques and synthetic minority oversampling technique (SMOTE) as the data sampling technique. In evaluating the performance of the random forest classifier on a credit card fraud dataset, our results show that CAE is a better feature extraction technique than PCA. The combination of CAE followed by SMOTE yields the best F1-score of 90.5%.