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A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification

contributor authorHong Cao
contributor authorTan, Vincent Y. F.
contributor authorPang, John Z. F.
date accessioned2020-03-12T23:52:05Z
date available2020-03-12T23:52:05Z
date issued2014
identifier issn2162-237X
identifier other6766252.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1126197?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleA Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification
typeJournal Paper
contenttypeMetadata Only
identifier padid8305987
subject keywordsGaussian processes
subject keywordscomputational complexity
subject keywordslearning (artificial intelligence)
subject keywordspattern classification
subject keywordssampling methods
subject keywordstime series
subject keywordstrees (mathematics)
subject keywordsF-value metric
subject keywordsGaussian trees model
subject keywordsclassification accuracy
subject keywordsclassification experiments
subject keywordscomputational complexity
subject keywordscovariance parameters
subject keywordsimbalanced time-series classification
subject keywordsmultimodal minority class
subject keywordsmultimodal time-series classification
subject keywordsoversampling
subject keywordsparsimonious statistical model
subject keywordssupport vector machines classifier
subject keywordsComputational m
identifier doi10.1109/TNNLS.2014.2308321
journal titleNeural Networks and Learning Systems, IEEE Transactions on
journal volume25
journal issue12
filesize3595369
citations1


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