A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection

Abstract

Stacking and bagging are widely used ensemble learning approaches that make use of multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous classifiers while bagging constructs an ensemble of homogenous classifiers. There exist some applications where it is essential for learning algorithms to be adaptive towards the training data. We propose A-Stacking and A-Bagging; adaptive versions of stacking and bagging respectively that take into consideration the similarity inherently present in the dataset. One of the main motives of ensemble learning is to generate an ensemble of multiple “experts” that are weakly correlated. We achieve this by producing a set of disjoint experts where each expert is trained on a different subset of the dataset. We show the working mechanism of the proposed algorithms on spoof fingerprint detection. The proposed versions of these algorithms are adaptive as they conform to the features extracted from the live and spoof fingerprint images. From our experimental results, we establish that A-Stacking and A-Bagging give competitive results on both balanced and imbalanced datasets.