Abstract
Fuzzy decision tree (FDT) is an extension of decision tree. Fuzzy classification rules can be extracted by FDT from fuzzy decision tables with fuzzy conditional attributes and fuzzy decision attributes. However, it is very time consuming for fuzzifying conditional attributes, and fuzzification of conditional attributes will inevitably lead to information loss. In order to deal with this problem, based on tolerance rough fuzzy set, this paper proposed an algorithm named TRFDT (Tolerance Rough Fuzzy Decision Tree) and theoretically proved that the proposed algorithm is convergent with a very large probability. TRFDT can directly handle fuzzy decision tables with continuous-valued conditional attributes and fuzzy decision attributes. Accordingly, TRFDT has fast learning speed and good generalization ability, which have been experimentally proved by comparing TRFDT with two state-of-the-art approaches fuzzy ID3 and FDT-YS.
| Original language | English |
|---|---|
| Pages (from-to) | 425-438 |
| Number of pages | 14 |
| Journal | Information Sciences |
| Volume | 465 |
| Early online date | 19 Jul 2018 |
| DOIs | |
| Publication status | Published - Oct 2018 |
| Externally published | Yes |
Bibliographical note
This research is supported by Basic Research Project of Knowledge Innovation Program in Shenzhen (JCYJ20150324140036825), by National Natural Science Foundations of China (71371063), and by the Natural Science Foundation of Hebei Province (F2017201026).Keywords
- Fuzzy decision tree
- Rough fuzzy set
- Rough set
- Tolerance rough fuzzy set
- Tolerance rough set