ElasticEnsemble¶
-
class
sktime.classification.distance_based.
ElasticEnsemble
(distance_measures='all', proportion_of_param_options=1.0, proportion_train_in_param_finding=1.0, proportion_train_for_test=1.0, n_jobs=None, random_state=0, verbose=0)[source]¶ The Elastic Ensemble (EE) as described in Jason Lines and Anthony Bagnall, “Time Series Classification with Ensembles of Elastic Distance Measures”, Data Mining and Knowledge Discovery, 29(3), 2015.
https://link.springer.com/article/10.1007/s10618-014-0361-2
Overview:
Input n series length m
EE is an ensemble of elastic nearest neighbor classifiers
Note
For the original Java version, see ElasticEnsemble.
- Parameters
distance_measures (list of strings, optional (default="all")) – A list of strings identifying which distance measures to include.
proportion_of_param_option (float, optional (default=1)) – The proportion of the parameter grid space to search optional.
proportion_train_in_param_finding (float, optional (default=1)) – The proportion of the train set to use in the parameter search optional.
proportion_train_for_test (float, optional (default=1)) – The proportion of the train set to use in classifying new cases optional.
n_jobs (int or None, optional (default=None)) – The number of jobs to run in parallel for both fit and predict.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors.random_state (int, default=0) – The random seed.
verbose (int, default=0) – If
>0
, then prints out debug information.