Abstract: A novel Spatial Overlapping based Semi-supervised Feature Selection（SOS-FS） method is proposed.It uses both labeled and unlabeled data in feature selection，feature’s relevance is measured by its overlapping ratio among different clusters.Experimental results carried out on some public datasets collected from the UCI machine learning repository and predictive toxicology domain show that SOS-FS has a promising performance on the improvement of the learning accuracy.