A fast conformal classifier based on multi-output extreme learning machine

A conformal classifier is a conformal prediction based classifier. Although the prediction is highly valid, the learning time of conformal classifiers is often very long due to the limitation of the computational framework. To make the conformal classifier learn faster, this paper firstly proposes an algorithm combining the conformal prediction with the multi-output extreme learning machine whose leave-one-out predictions on the training set can be computed efficiently. From the analysis of algorithm complexity, the computational complexity of the proposed algorithm is equivalent to that of the multi-output extreme learning machine. The experiments on ten public data sets show that the proposed our algorithm has fast computation speed and inherits the property of validity from conformal prediction, whose prediction error can be controlled by the significance level. The average number of labels per prediction of the proposed algorithm is lower than that of other common conformal classifiers on some data sets, which shows that of is more efficient in some applications. © 2019, Editorial Office of Control and Decision. All right reserved.