Classifier

HIVECOTEV1

Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1.

Quickstart

python

from sktime.classification.hybrid import HIVECOTEV1

estimator = HIVECOTEV1(stc_params=None, tsf_params=None, rise_params=None, cboss_params=None, verbose=0, n_jobs=1, random_state=None)

Parameters(7)

stc_paramsdict or None, default=None: Parameters for the ShapeletTransformClassifier module. If None, uses the default parameters with a 2 hour transform contract.
tsf_paramsdict or None, default=None: Parameters for the TimeSeriesForestClassifier module. If None, uses the default parameters with n_estimators set to 500.
rise_paramsdict or None, default=None: Parameters for the RandomIntervalSpectralForest module. If None, uses the default parameters with n_estimators set to 500.
cboss_paramsdict or None, default=None: Parameters for the ContractableBOSS module. If None, uses the default parameters.
verboseint, default=0: Level of output printed to the console (for information only).
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_stateint or None, default=None: Seed for random number generation.

References

[1 ] (1, 2) Anthony Bagnall, Michael Flynn, James Large, Jason Lines and Matthew Middlehurst. “On the usage and performance of the Hierarchical Vote Collective of Transformation-based Ensembles version 1.0 (hive-cote v1.0)” International Workshop on Advanced Analytics and Learning on Temporal Data 2020 [2 ] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. “HIVE-COTE 2.0: a new meta ensemble for time series classification.” Machine Learning (2021).