BOSSEnsemble

class sktime.classification.dictionary_based.BOSSEnsemble(threshold=0.92, max_ensemble_size=500, max_win_len_prop=1, min_window=10, n_jobs=1, random_state=None)[source]

Bag of SFA Symbols (BOSS)

Bag of SFA Symbols Ensemble: implementation of BOSS from [1]

Overview: Input n series length m BOSS performs a gird search over a set of parameter values, evaluating each with a LOOCV. It then retains all ensemble members within 92% of the best. There are three primary :param alpha: alphabet size :param w: window length :param l: word length.

for any combination, a single BOSS slides a window length w along the series. The w length window is shortened to an l length word through taking a Fourier transform and keeping the first l/2 complex coefficients. These l coefficients are then discretised into alpha possible values, to form a word length l. A histogram of words for each series is formed and stored. fit involves finding n histograms.

predict uses 1 nearest neighbour with a bespoke distance function.

Parameters
  • threshold (double [0,1] retain all classifiers within) –

  • of the best one (threshold%) –

  • (default = 0.92) (optional) –

  • max_ensemble_size (int or None, retain a maximum number of) –

  • classifiers

  • if within threshold (even) –

  • (default = 500) (optional) –

  • max_win_len_prop (maximum window length as a proportion of) –

  • length (default = 1) (series) –

  • min_window (minimum window size, (default = 10)) –

  • n_jobs (int, optional (default=1)) –

  • number of jobs to run in parallel for both fit and predict. (The) –

  • means using all processors. (-1) –

  • random_state (int or None, seed for random, integer,) –

  • (default to no seed) (optional) –

n_classes[source]
Type

extracted from the data

n_instances[source]
Type

extracted from the data

n_estimators[source]
Type

The final number of classifiers used (

<= max_ensemble_size)
series_length[source]
Type

length of all series (assumed equal)

classifiers[source]
Type

array of DecisionTree classifiers

Notes

..[1] Patrick Schäfer, “The BOSS is concerned with time series classification

in the presence of noise”, Data Mining and Knowledge Discovery, 29(6): 2015 https://link.springer.com/article/10.1007/s10618-014-0377-7

For the Java version, see https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/dictionary_based/BOSS.java

__init__(threshold=0.92, max_ensemble_size=500, max_win_len_prop=1, min_window=10, n_jobs=1, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.