The object class intrinsic filter conjecture

Abstract

We present the object class intrinsic filter conjecture. The aim of our work is to investigate design principles for machine learning algorithms that demonstrate false positive rates practically equal to zero. Reducing the false positive rate in machine learning algorithms is important in several fields. In cybersecurity for instance, algorithms that demonstrate few to almost zero false positive detections are typically more robust against attacks, where the adversary intentionally modifies the input in order to drive algorithms to some intended misclassification result. The object class intrinsic filter conjecture states that it is possible to augment standard machine learning algorithms with filters which depend on the object class which is being detected, and which, contrary to standard machine learning algorithms, use weight, bias and threshold values which are in part determined through training and in part functions of the runtime input (i.e., the input provided to algorithms at runtime for classification or scoring). This is a departure from some standard machine learning algorithms where weight and threshold values are determined only through training. We investigate the validity of our conjecture experimentally, presenting a hand gesture recognition system augmented with an object class intrinsic filter where the overall system's false positive rate is dropped to practically zero.