Modern patient care aims for individualized solutions. Current machine learning techniques, in general and in the medical domain, typically incorporate big amounts of data. In fact, more data contributes to the generalizability of said techniques. However, it might conflict with the desire for individualized solutions. Our works aim at the implementation of individual solutions based on machine learning techniques. Within this contribution, we investigate the potential benefit of individualized classifiers in the context of automatic sleep staging using cardiorespiratory features.To that end, we performed sleep stage classification using 237 records of the Sleep Heart Health Study. For each patient, we trained an ensemble classifier that is based on a subset of the available patients. Such subsets of varying size were chosen by a modified version of sequential forward floating selection. Our results show that the individualized classifier improves classification compared to a classifier that uses all available patients by 30% (improvement in Cohen's kappa coefficient (κ) of 0.15 from 0.46 to 0.61). On average the subset used for training thereby includes five patients.The presented contribution clearly depicts the potential of an individualized classification approach. Based on the current results, future works will try to establish metrics that can identify the most appropriate training subset in an unsupervised way.