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Meta-Learning for Hierarchical Classification and Applications in Bioinformatics

Fabris, Fabio (2018) Meta-Learning for Hierarchical Classification and Applications in Bioinformatics. International Journal of Computer and Information Engineering, 12 (7). ISSN 2010-3921.

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Abstract

Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Item Type: Article
Uncontrolled keywords: Algorithm recommendation, meta-learning, bioinformatics, hierarchical classification
Subjects: Q Science
Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 14 Sep 2018 14:38 UTC
Last Modified: 29 May 2019 21:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69105 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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