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Journal Articles Information Year : 2020

Kernel-Based Ensemble Learning in Python

Abstract

We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce \texttt{KernelCobra}, a non-linear learning strategy for combining an arbitrary number of initial predictors. \texttt{KernelCobra} builds on the COBRA algorithm introduced by \citet{biau2016cobra}, which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalize this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and \texttt{KernelCobra} systematically outperforms the COBRA algorithm. While COBRA is intended for regression, \texttt{KernelCobra} deals with classification and regression. \texttt{KernelCobra} is included as part of the open source Python package \texttt{Pycobra} (0.2.4 and onward), introduced by \citet{guedj2018pycobra}. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of \texttt{KernelCobra} on real-life and synthetic datasets.
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Dates and versions

hal-02443097 , version 1 (16-01-2020)
hal-02443097 , version 2 (18-02-2020)

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Cite

Benjamin Guedj, Bhargav Srinivasa Desikan. Kernel-Based Ensemble Learning in Python. Information, 2020, 11 (2), pp.63. ⟨10.3390/info11020063⟩. ⟨hal-02443097v2⟩
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