TY - CHAP

T1 - The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy

AU - Flynn, Michael

AU - Large, James

AU - Bagnall, Anthony

PY - 2019

Y1 - 2019

N2 - 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(nm2)O(nm2), 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.

AB - 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(nm2)O(nm2), 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.

U2 - 10.1007/978-3-030-29859-3_33

DO - 10.1007/978-3-030-29859-3_33

M3 - Chapter

SN - 978-3-030-29858-6

T3 - Lecture Notes in Computer Science

SP - 381

EP - 392

BT - International Conference on Hybrid Artificial Intelligence Systems

A2 - Pérez García, Hilde

A2 - Sánchez González, Lidia

A2 - Castejón Limas, Manuel

A2 - Quintián Pardo, Héctor

A2 - Corchado Rodríguez, Emilio

PB - Springer

ER -