Safe Classification with Augmented Features

With the evolution of data collection methods, it is possible to produce abundant data described by multiple feature sets. Previous studies show that including more features does not necessarily bring positive effects. How to prevent the augmented features from worsening classification performance is crucial but rarely studied. In this paper, we study this challenging problem by proposing a safe classification approach, whose accuracy is never degenerated when exploiting augmented features. We propose two ways to achieve the safeness of our method named as SAfe Classification (SAC). First, to leverage augmented features, we learn various types of classifiers and adapt them by employing a specially designed robust loss. It provides various candidate classifiers to meet the assumption of safeness operation. Second, we search for a safe prediction by integrating all candidate classifiers. Under a mild assumption, the integrated classifier has theoretical safeness guarantee. Several new optimization methods have been developed to accommodate the problems with proved convergence. Besides evaluating SAC on 16 data sets, we also apply SAC in the application of diagnostic classification of schizophrenia since it has vast application potentiality. Experimental results demonstrate the effectiveness of SAC in both tackling safeness problem and discriminating schizophrenic patients from healthy controls.