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Found 5 papers, 4 papers with code
LCE: An Augmented Combination of Bagging and Boosting in Python
lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression.
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
Traffic classification, i. e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e. g., intrusion detection, routing).
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability.
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods.
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification.
