Abstract
Fuzzy-rough sets play an important role in dealing with imprecision
and uncertainty for discrete and real-valued or noisy data. However,
there are some problems associated with the approach from both
theoretical and practical viewpoints. These problems have motivated
the hybridisation of fuzzy-rough sets with kernel methods. Existing
work which hybridises fuzzy-rough sets and kernel methods employs a
constraint that enforces the transitivity of the fuzzy $T$-norm
operation. In this paper, such a constraint is relaxed and a new
kernel-based fuzzy-rough set approach is introduced. Based on this,
novel kernel-based fuzzy-rough nearest-neighbour algorithms are
proposed. The work is supported by experimental evaluation, which
shows that the new kernel-based methods offer improvements over the
existing fuzzy-rough nearest neighbour classifiers.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 20th IEEE International Conference on Fuzzy Systems |
| Publisher | IEEE Press |
| Pages | 1523-1529 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 06 Sept 2011 |
| Event | Fuzzy Systems - Taipei, Taiwan Duration: 27 Jun 2011 → 30 Jun 2011 Conference number: 20 |
Conference
| Conference | Fuzzy Systems |
|---|---|
| Abbreviated title | FUZZ-IEEE-2011 |
| Country/Territory | Taiwan |
| City | Taipei |
| Period | 27 Jun 2011 → 30 Jun 2011 |