Classifier

HIVECOTEV1

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

An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature representations using the CAWPE structure as described in [1]. The default implementation differs from the one described in [1], in that the STC component uses the out of bag error (OOB) estimates for weights (described in [2]) rather than the cross validation estimate. OOB is an order of magnitude faster and on average as good as CV. This means that this version of HIVE COTE is a bit faster than HC2, although less accurate on average.

Schnellstart

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)

Parameter(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.

Referenzen

[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).