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Meta‐learning and the new challenges of machine learning
Author(s) -
Monteiro José Pedro,
Ramos Diogo,
Carneiro Davide,
Duarte Francisco,
Fernandes João M.,
Novais Paulo
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22549
Subject(s) - computer science , retraining , machine learning , task (project management) , artificial intelligence , selection (genetic algorithm) , meta learning (computer science) , value (mathematics) , streaming data , model selection , data mining , management , international trade , economics , business
In the last years, organizations and companies in general have found the true potential value of collecting and using data for supporting decision‐making. As a consequence, data are being collected at an unprecedented rate. This poses several challenges, including, for example, regarding the storage and processing of these data. Machine Learning (ML) is also not an exception, in the sense that algorithms must now deal with novel challenges, such as learn from streaming data or deal with concept drift. ML engineers also have a harder task when it comes to selecting the most appropriate model, given the wealth of algorithms and possible configurations that exist nowadays. At the same time, training time is a stronger restriction as the computational complexity of the training model increases. In this paper we propose a framework for dealing with these challenges, based on meta‐learning. Specifically, we tackle two well‐defined problems: automatic algorithm selection and continuous algorithm updates that do not require the retraining of the whole algorithm to adapt to new data. Results show that the proposed framework can contribute to ameliorate the identified issues.