VSG3A2: A Genetic Algorithm-Based Virtual Sample Generation Approach Using Information Gain and Acceptance-Rejection Sampling

Virtual sample generation (VSG) is an important technology for dealing with small sample learning in some industries. Using evolutionary computation algorithms to solve virtual sample generation is a promising way. However, two issues remain unaddressed in the existing VSG approaches: estimating the distribution of original samples and ensuring the authenticity of virtual samples. Thus, this paper proposes a novel Virtual Sample Generation approach based on genetic algorithm (GA) combing with Information Gain and Acceptance-rejection Sampling, abbreviated as VSG3A2. First, this work develops the ARS-VAD subalgorithm, by integrating the acceptance rejection sampling method into the crossover and mutation operations of GA. The algorithm ensures that the distribution of offspring attribute values is close to the distribution of original samples at attribute level. Second, this work presents the IG-VSS subalgorithm, which is combined with the idea of minimizing absolute information gain, to find the optimal offspring sample as a virtual sample in each loop, ensuring the authenticity of virtual samples at the sample level. To the best of our knowledge, this is the first work that introduces the concept of information gain into VSG. Extensive experiments on four public datasets from various fields fully demonstrate that the VSG3A2 is more competitive than six state-of-the-art VSG approaches. The MAE, the RMSE and the MAPE metrics of prediction models, trained on virtual samples generated by the proposed VSG3A2, are reduced at least by 19.78%, 19.56%, 20.16% on average than that of the best compared VSG approach, respectively.