BRAINSTORMING: Consensus Learning in Practice

TL;DR

This paper introduces Brainstorming, a consensus meta-learning approach that combines diverse machine learning models trained on different data representations, enhancing robustness and performance in bioinformatics and chemoinformatics applications.

Contribution

It proposes a novel ensemble method that integrates heterogeneous classifiers through late-stage consensus, improving predictive accuracy and reliability.

Findings

Effective in bioinformatics applications

Enhances robustness of predictions

Balances model diversity and performance

Abstract

We present here an introduction to Brainstorming approach, that was recently proposed as a consensus meta-learning technique, and used in several practical applications in bioinformatics and chemoinformatics. The consensus learning denotes heterogeneous theoretical classification method, where one trains an ensemble of machine learning algorithms using different types of input training data representations. In the second step all solutions are gathered and the consensus is build between them. Therefore no early solution, given even by a generally low performing algorithm, is not discarder until the late phase of prediction, when the final conclusion is drawn by comparing different machine learning models. This final phase, i.e. consensus learning, is trying to balance the generality of solution and the overall performance of trained model.