@article{(Open Science Index):https://publications.waset.org/pdf/10009269,
	  title     = {Meta-Learning for Hierarchical Classification and Applications in Bioinformatics},
	  author    = {Fabio Fabris and  Alex A. Freitas},
	  country	= {},
	  institution	= {},
	  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
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {12},
	  number    = {7},
	  year      = {2018},
	  pages     = {535 - 545},
	  ee        = {https://publications.waset.org/pdf/10009269},
	  url   	= {https://publications.waset.org/vol/139},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 139, 2018},