Abstract
One of the many successful applications
of rough set theory has been to the area
of feature selection. The rough set ideology
of using only the supplied data and
no other information has many benefits,
where most other methods require supplementary knowledge. Fuzzy-rough set
theory has recently been proposed as an
extension of this, in order to better handle
the uncertainty present in real data.
However, following this approach, there
has been no investigation (theoretical or
otherwise) into how to deal with missing
values effectively, another problem encountered when using real world data.
This paper proposes an extension of the
fuzzy-rough feature selection methodology,
based on interval-valued fuzzy sets,
as a means to counter this problem via
the representation of missing values in an
intuitive way.
| Original language | English |
|---|---|
| Title of host publication | 8th Annual UK Workshop on Computational Intelligence (UKCI'08) |
| Pages | 59-64 |
| Number of pages | 6 |
| Publication status | Published - 2008 |
| Event | 8th Annual UK Workshop on Computational Intelligence (UKCI'08) - Leicester, United Kingdom of Great Britain and Northern Ireland Duration: 10 Sept 2008 → 12 Sept 2008 |
Conference
| Conference | 8th Annual UK Workshop on Computational Intelligence (UKCI'08) |
|---|---|
| Country/Territory | United Kingdom of Great Britain and Northern Ireland |
| City | Leicester |
| Period | 10 Sept 2008 → 12 Sept 2008 |