The distance of probabilistic fuzzy sets for classification
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 2157-2165 |
| Journal / Publication | Pattern Recognition Letters |
| Volume | 34 |
| Issue number | 16 |
| Publication status | Published - 2013 |
Link(s)
Abstract
The probabilistic fuzzy set (PFS) is designed for handling the uncertainties with both stochastic and fuzzy nature. In this paper, the concept of the distance between probabilistic fuzzy sets is introduced and its metric definition is conducted, which may be finite or continuous. And some related distances are discussed. The proposed distance considers the random perturbation in progress by introducing the distance of probability distribution, thus it improves the ability to handle random uncertainties, and some inadequacy of the distance of probability distribution is remedied. Finally, a PFS-based distance classifier is proposed to discuss the classification problem, the numerical experiment shows the superiority of this proposed distance in fuzzy and stochastic circumstance. © 2013 Elsevier B.V. All rights reserved.
Research Area(s)
- FS-based distance, PFS-based distance, Probabilistic fuzzy set, The distance between probabilistic fuzzy sets
Citation Format(s)
The distance of probabilistic fuzzy sets for classification. / Huang, Wen-Jing; Zhang, Geng; Li, Han-Xiong.
In: Pattern Recognition Letters, Vol. 34, No. 16, 2013, p. 2157-2165.
In: Pattern Recognition Letters, Vol. 34, No. 16, 2013, p. 2157-2165.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
