Outlier Interpretation Using Regularized Auto Encoders and Genetic Algorithm

Outlier interpretation is essential in various data processing fields within the context of outlier detection. Under-standing the specific attributes that make an outlier distinct is particularly valuable in many application areas, especially where, due to class imbalance, it can greatly enhance analytic insights into unusual occurrences or trends. To address this need, we introduce a Regularized Auto-Encoder with a Genetic Algorithm (RAE-GA) approach for outlying and characterizing subspaces: i.e. a novel approach for identifying outliers and pinpointing their underlying subspaces. This method leverages autoencoders with regularized latent spaces, such as in Variational Auto Encoders (VAE) and Regularized Auto Encoders (RAE), which have proven effective in outlier detection only. So, building upon this success, the proposed RAE-GA utilizes a specially crafted, computationally efficient fitness function based on RAE. This function is integrated into the genetic algorithm to explore various potential subspaces, eliminating the need for restrictive pruning or constraints. As our experimental results indicate, RAE-GA outperforms the current state-of-the-art methods in outlier interpretation.