[ENH] Implement the feature-based TD-MVDC classification algorithm

[CCHe64]

Describe the feature or idea you want to propose

Tracking differentiator-based multiview dilated characteristics (TD-MVDC) is a new feature-based TSC algorithm.

The introduction of a tracking differentiator combined with dilation mapping as a preprocessor into the feature-based TSC method is proposed to improve feature diversity of TSFresh efficiently.
Ensemble feature selection based on filter feature selectors with different store ratios is designed to generate multiview features to enhance feature stability quickly.
Linear classifiers and hard voting to fastly classify and integrate multiview features to increase classification performance robustly.

The paper title is " Tracking Differentiator-based Multiview Dilated Characteristics for Time Series Classification" and has been peer-reviewed and presented at the INDIN24 conference.
The paper file and Python code can be obtained below:
https://github.com/CCHe64/TD-MVDC
Preprinted version:
https://github.com/CCHe64/TD-MVDC/blob/main/Tracking%20Differentiator-based%20Multiview%20Dilated%20Characteristics%20for%20Time%20Series%20Classification.pdf
TD-MVDC is implemented through functions in sklearn and aeon.
Python source code is here:
https://github.com/CCHe64/TD-MVDC/blob/main/Tracking%20Differentiator-based%20Multiview%20Dilated%20Characteristics.ipynb

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

No response

[vedpawar2254]

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