Federated Ensemble Regression Using Classification
Paper i 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



Gene expression


Oghenejokpeme I. Orhobor

University of Cambridge

Larisa N. Soldatova

Goldsmiths, University of London

Ross King

Alan Turing Institute

University of Cambridge

Chalmers, Biologi och bioteknik, Systembiologi

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,


Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Datorseende och robotik (autonoma system)



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