Relieff for hierarchical multi-label classification
Date Issued
2013-09-27
Author(s)
Slavkov, Ivica
Karcheska, Jana
Kocev, Dragi
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.
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.
Subjects
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