Interpretable tabular data generation

Abstract

AbstractGenerative adversarial network () models have been successfully utilized in a wide range of machine learning applications, and tabular data generation domain is not an exception. Notably, some state-of-the-art models of tabular data generation, such as ,  , , etc. are based on models. Even though these models have resulted in superior performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. For example, the current methods focus only on the performance of the model, and limited emphasis is given on the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model—GenerativeAdversarial Network modelling inspired fromNaiveBayes andLogisticRegression’s relationship ($${ { \texttt {GANBLR} } }$$ GANBLR ), which not only address the interpretation limitation of existing tabular -based models but provides capability to handle explicit feature interactions as well. Through extensive evaluations on wide range of datasets, we demonstrate $${ { \texttt {GANBLR} } }$$ GANBLR ’s superior performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.