Secure Classification With Augmented Features

TL;DR

This paper introduces a secure classification method that ensures augmented features do not degrade accuracy, using multiple classifiers and robust optimization, with theoretical guarantees and practical validation on diverse datasets.

Contribution

It proposes a novel secure classification framework that maintains accuracy with augmented features through classifier ensemble and robust loss functions, backed by theoretical security guarantees.

Findings

SEC maintains classification accuracy with augmented features.

SEC outperforms baseline methods on 16 datasets.

Effective in diagnosing schizophrenia from clinical data.

Abstract

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.