We introduce MSS to improve the performance of EI-MS spectrum prediction for minor data using the NEIMS based model. A large number of bit collisions occurred in the ECFPs for NIST library, which indicates that learning would be effectively promoted by mitigating bit collisions. Our numerical assessment demonstrates that the model was trained for reduced data chemically close to the target compounds and could predict the mass spectra for the target and nontarget compounds with high and reasonable accuracies. Although this study adopted highly fluorinated compounds as targets for predicting mass spectra, the undersampling approach is applicable to other chemical compound for other spectra and physical properties. And, the undersampling is regarded as an automatic cleansing method for molecular big data. When the vector length was simply extended to reduce bit collisions, the data became sparse, and learning of ResNets did not progress. Also, if input dimensions are indiscriminately increased to resolve bit collisions, the amount of memory required will also be enormous, so it is not ideal. Since Graph Neural Networks learns expressions directly from graphs without going through descriptors such as ECFPs, there is no problem with bit collisions, and it is thought that there is a possibility of improvement. In fact, Zhang et al. recently reported on improving the architecture using Graph Neural Networks . In future research, we plan to investigate the applicability of MSS to Graph Neural Networks and design a loss function based on molecular similarity reflecting the concept of MSS.