Noise filter with hyperparameter recommendation: a meta-learning approach

  • Pedro B. Pio Universidade de Brasília (UnB)
  • Adriano Rivolli Universidade Tecnológica Federal do Paraná (UTFPR)
  • André C. P. L. F. de Carvalho Universidade de São Paulo (USP)
  • Luís P. F. Garcia Universidade de Brasília (UnB)


Applying Machine Learning (ML) algorithms to a dataset can be time-consuming. It usually involves, not only selecting and fine-tuning the algorithm, but also other steps, such as data preprocessing. To reduce this time, the whole or a subset of this process has been automated by Automated ML (AutoML) techniques, which can include Bayesian Optimization, Genetic Programming, and Meta-Learning techniques. However, despite it often being a necessary stage, preprocessing is commonly not well handled in AutoML tools. In this work, we propose and experimentally investigate the use of meta-learning to recommend noise detection algorithms and the values for their hyperparameters. The proposed approach produces a ranking of the best noise filters for a given dataset, reducing the development cost of ML-based solutions and improving their predictive performance. To validate the process, we generated 10740 noisy datasets, which we describe using 97 meta-features. For each dataset, we applied 8 noise filters, which increased to 27 when we added variations of hyperparameter values. Next, we applied 4 ML algorithms to this data and created a performance ranking, which we used as a meta-target to induce 3 meta-regressors. We compared these 3 meta-regressors and the results with and without hyperparameters for the noise filters. According to the experimental results, the introduction of hyperparameter recommendation resulted in a higher gain in the F1-Score performance metric. However, it came at the cost of lower accuracy in the Top-K ranking evaluation.

Palavras-chave: AutoML, Meta-Learning, Noise Detection


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PIO, Pedro B.; RIVOLLI, Adriano; CARVALHO, André C. P. L. F. de; GARCIA, Luís P. F.. Noise filter with hyperparameter recommendation: a meta-learning approach. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 625-639. ISSN 2763-9061. DOI: