The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy

Abstract

The Random Interval Spectral Ensemble (RISE) is a recently introduced tree based time series classification algorithm, in which each tree is built on a distinct set of Fourier, autocorrelation and partial autocorrelation features. It is a component in the meta ensemble HIVE-COTE [9]. RISE has run time complexity of $O(nm^2)$, where m is the series length and n the number of train cases. This is prohibitively slow when considering long series, which are common in problems such as audio classification, where spectral approaches are likely to perform better than classifiers built in the time domain. We propose an enhancement of RISE that allows the user to specify how long the algorithm can have to run. The contract RISE (c-RISE) allows for check-pointing and adaptively estimates the time taken to build each tree in the ensemble through learning the constant terms in the run time complexity function. We show how the dynamic approach to contracting is more effective than the static approach of estimating the complexity before executing, and investigate the effect of contracting on accuracy for a range of large problems.