World Academy of Science, Engineering and Technology
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