Early classiﬁcation of time series will weaken the accuracy to some degree. If the time series data are imbalanced, it will be also challenging to accurately identify minority class examples. Up to now, these two problems have been intensively addressed separately on univariate time series data, but yet to be well studied when they occur together. Compared with univariate time series, multivariate time series (MTS) is more complex, which contains multiple variables, and the interconnections between variables are hidden. Therefore, it is even more challenging to handle the combination of both problems on multivariate time series. In this paper, we propose an adaptive classiﬁcation ensemble method called early prediction on imbalanced MTS to deal with early classiﬁcation on inter-class and intra-class imbalanced MTS data simultaneously. First, an adaptive ensemble framework is designed to learn an early classiﬁcation model on imbalanced MTS data. Based on a multiple under-sampling approach and dynamical subspace generation method, the diversity of base classiﬁers is realized as well as all majority class examples being fully utilized. Second, to deal with the implicit issue of intra-class imbalance in the training data, a cluster-based shapelet selection method is introduced to obtain an optimal set of stable and
Soft Computing – Springer Journals
Published: Jun 4, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera