Fabris, Fabio (2018) Meta-Learning for Hierarchical Classification and Applications in Bioinformatics. International Journal of Computer and Information Engineering, 12 (7). ISSN 2010-3921. (KAR id:69105)
PDF
Publisher pdf
Language: English |
|
Download this file (PDF/196kB) |
![]() |
Request a format suitable for use with assistive technology e.g. a screenreader | |
PDF
Author's Accepted Manuscript
Language: English |
|
Download this file (PDF/251kB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader |
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: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Alex Freitas |
Date Deposited: | 14 Sep 2018 14:38 UTC |
Last Modified: | 10 Dec 2022 02:12 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/69105 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):