Meta-Learning for Hierarchical Classification and Applications in Bioinformatics

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.