Subject: feature selection, feature ranking, feature relevance, structured data, hierarchical multi-label classification, multi-label classification, ReliefF
Year: 2013
Type: Proceeding article
Title: Relieff for hierarchical multi-label classification
Author: Slavkov, Ivica
Author: Karcheska, Jana
Author: Kocev, Dragi
Author: Kalajdziski, Slobodan
Author: Džeroski, Sasho
Abstract: In the recent years, the data available for analysis in machine learning is becoming very high-dimensional and also structured in a more complex way. This emphasises the need for developing machine learning algorithms that are able to tackle both the high-dimensionality and the complex structure of the data. Our work in this paper, focuses on extending a feature ranking algorithm that can be used as a filter method for specific type of structured data. More specifically, we adapt the RReliefF algorithm for regression, for the task of hierarchical multi-label classification (HMC). We evaluate this algorithm experimentally in a filter-like setting by employing PCTs for HMCs as a classifier and we consider datasets from various domains. The results show that HMC-ReliefF can identify the relevant features present in the data and produces a ranking where they are among the top ranked.
Publisher: Springer, Cham
Relation: International Workshop on New Frontiers in Mining Complex Patterns
Identifier: oai:repository.ukim.mk:20.500.12188/24076
Identifier: http://hdl.handle.net/20.500.12188/24076
