Модифицированным метод классификации многомерных временных рядов с использованием шейплетов

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А.П. Карпенко, П.И. Сотников
64 ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2017. № 2
Abstract Keywords
We consider the classification of multivariate time series
using a paradigm, called shapelets. Instead of exhaustive
search among all subsequences of the original time series, we
suggest using a genetic algorithm for shapelets discovering.
The problem of shapelets discovering is considered as a
one-criterion optimization task. The quality of candidates
acts as an objective function. Variable parameters are candi-
date attributes that define their position in the original
dataset. We also propose measuring the quality of shapelets
by assessing the classification accuracy. The assessment is
made on a new dataset, where each object represents the
distance vector from a shapelet to original time series. We
evaluate efficiency of the proposed method modifications on
the known electroencephalogram (EEG) recordings obtained
for subjects performing a spelling task with P300-based
brain-computer interface (BCI). The results show that these
modifications can reduce the search space by nearly 99%
with no loss of classification accuracy
Time series, classification, shapelets,
genetic algorithm, brain-computer
interface
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