In the spectral prediction task, the prediction accuracy tends to be lower for minor compounds such as highly fluorinated compounds. It is due to the fact that the ECFPs used to represent NEIMS data have a representation issue, namely, bit collision (Figure ) and data imbalance. The most straightforward and commonly used approach to this type of issue is sampling methods such as oversampling and undersampling , but mainstream sampling methods are designed for labeled classification tasks and cannot be directly applied to regression tasks such as mass spectral prediction. We assumed the presence of arbitrary multiple target compounds, calculated the molecular similarity to them for the entire dataset, labeled the data with a threshold, and undersampled the data with a low similarity. The Tanimoto coefficients of two sets of molecular fingerprints is used as a similarity: