Description
Describe the feature or idea you want to propose
Multiview Enhanced Characteristics (Mecha) is a new feature-based TSC algorithm.
Mecha consists of three components. In the diverse feature extractor, the global and local patterns are enhanced via shuffling mapping with dilation and interleaving mechanisms, improving the feature diversity and expressiveness.
In the ensemble feature selector, diverse and stable multiview features are adaptively generated by multiple filters and intersections based on the feature stability and diversity scores.
In the heterogeneous ensemble classifier, the ridge regression with cross-validation and extremely randomized trees classifiers are integrated via hard voting to enhance classifier diversity.
The paper title is "Mecha: Multiview Enhanced Characteristics via Series Shuffling for Time Series Classification and Its Application to Turntable Circuit," and it has been accepted and is available with early access.
The paper file can be obtained below:
https://ieeexplore.ieee.org/document/11068941
TD-MVDC is implemented through functions in sklearn and aeon.
Python source code is here:
https://github.com/CCHe64/Mecha
Describe your proposed solution
Firstly, in aeon implement a new feature extraction transformation function using TSFreshEigenExtractor in transformations. collection.feature_based.
Then implement a new classification function in aeon. classification.feature_based.
Describe alternatives you've considered, if relevant
No response
Additional context
Mecha is an upgraded version of TD-MVDC previously implemented in aeon.