A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

Abstract

Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, this paper reports on a study on how to make an existing algorithm, named C3E (from consensus between classification and clustering ensembles), more convenient by automatically tuning its main parameters. The C3E algorithm is based on a general optimisation framework that takes as input class membership estimates from existing classifiers, and a similarity matrix from a cluster ensemble operating solely on the new (target) data to be classified, in order to yield a consensus labelling of the new data. To do so, two parameters have to be defined a priori by the user: the relative importance of classifier and cluster ensembles, and the number of iterations of the algorithm. We propose a differential evolution (DE) algorithm, named dynamic DE (D2E), which is a computationally efficient alternative for optimising such parameters. D2E provides better results than DE by dynamically updating its control parameters. Moreover, competitive results were achieved when comparing D2E with three state-of-the-art algorithms.