MOGT: oversampling with a parsimonious mixture of Gaussian trees model for imbalanced time-series classification

Published in IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013

Recommended citation: John Z. F. Pang, Hong Cao, Vincent Y. F. Tan (2013). "MOGT: oversampling with a parsimonious mixture of Gaussian trees model for imbalanced time-series classification." IEEE Machine Learning for Signal Processing. http://j-pang.github.io/files/mlsp2013_mogt.pdf

This paper is on using a mixture of gaussian trees statistical model to solve the problem of multi-modal imbalanced time-series classification.

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Recommended citation: John Z. F. Pang, Hong Cao, Vincent Y. F. Tan (2013). “MOGT: oversampling with a parsimonious mixture of Gaussian trees model for imbalanced time-series classification.” IEEE Machine Learning for Signal Processing.