Voting with random classifiers (VORACE): theoretical and experimental analysis
| Autor: | Francesca Rossi, Michele Donini, Cristina Cornelio, Andrea Loreggia, Maria Silvia Pini |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence media_common.quotation_subject Multi-agent Learning Machine Learning (stat.ML) Sample (statistics) 02 engineering and technology Machine learning computer.software_genre Machine Learning (cs.LG) Domain (software engineering) Machine Learning Set (abstract data type) Social Choice Theory Artificial Intelligence Computer Science - Computer Science and Game Theory Statistics - Machine Learning 020204 information systems Voting 0202 electrical engineering electronic engineering information engineering Computer Science - Multiagent Systems Ensemble Methods media_common business.industry Model selection Aggregate (data warehouse) Artificial Intelligence (cs.AI) Ranking 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) Computer Science and Game Theory (cs.GT) Multiagent Systems (cs.MA) |
| Zdroj: | Autonomous Agents and Multi-Agent Systems |
| ISSN: | 2020-2024 |
| Popis: | First published online: 21 May 2021 In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets. This article recieved funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 833647). This article was published Open Access with the support from the EUI Library through the CRUI - Springer Transformative Agreement (2020-2024) |
| Databáze: | OpenAIRE |
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