  With the evolution of data collection ways, it is possible to produce
abundant data described by multiple feature sets. Previous studies show that
including more features does not necessarily bring positive effect. How to
prevent the augmented features worsening classification performance is crucial
but rarely studied. In this paper, we study this challenging problem by
proposing a secure classification approach, whose accuracy is never degenerated
when exploiting augmented features. We propose two ways to achieve the security
of our method named as SEcure Classification (SEC). Firstly, 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 following assumption of security operation. Secondly,
we integrate all candidate classifiers by approximately maximizing the
performance improvement. Under a mild assumption, the integrated classifier has
theoretical security guarantee. Several new optimization methods have been
developed to accommodate the problems with proved convergence. Besides
evaluating SEC on 16 data sets, we also apply SEC in the application of
diagnostic classification of schizophrenia since it has vast application
potentiality. Experimental results demonstrate the effectiveness of SEC in both
tackling security problem and discriminating schizophrenic patients from
healthy controls.
