| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos, SP - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | INFORMATION SCIENCES; v. 477, p. 203-219, MAR 2019. |
| Web of Science Citations: | 2 |
| Abstract | |
Meta-learning has been successfully used for algorithm recommendation tasks. It uses machine learning to induce meta-models able to predict the best algorithms for a new dataset. In this paper, meta-models are applied to a set of meta-features, describing a dataset, to predict the performance of clustering algorithms applied to this dataset. The paper also proposes a new set of meta-features, based on correlation and dissimilarity measures. Experimental results show that these meta-features improve the recommendation. Additionally, this paper evaluates the importance of each meta-feature for the recommendation. (C) 2018 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 16/18615-0 - Advanced machine learning |
| Grantee: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 17/20265-0 - Use of meta-learning for clustering algorithm selection problems |
| Grantee: | Bruno Almeida Pimentel |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |