Federated Ensemble Regression Using Classification
Paper in proceeding, 2020

Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case.

Ensemble learning

Machine learning

Bioinformatics

Regression

Gene expression

Author

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University of Cambridge

[Person 3751344c-2aeb-44ad-8c6f-b9b212055d17 not found]

Goldsmiths, University of London

[Person b7594557-7eef-4a6e-835c-4077fe05de7e not found]

Alan Turing Institute

University of Cambridge

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12323 LNAI 325-339
9783030615260 (ISBN)

23rd International Conference on Discovery Science, DS 2020
Thessaloniki, Greece,

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/978-3-030-61527-7_22

More information

Latest update

11/26/2020