Name
Title | ||
|---|---|---|
Improving Micro-Extended Belief Rule-Based System Using Activation Factor for Classification Problems |
Abstract | ||
|---|---|---|
The micro-extended belief rule-based system (Micro-EBRBS) is an advanced rule-based system and has shown its superior ability in solving big data problems. To overcome the activation rule incompleteness and inconsistency of Micro-EBRBS, a new concept, named activation factor (AF), is introduced to revise the calculation of individual matching degree and, furthermore, an AF-based inference (AFI) method is proposed for improving Micro-EBRBS. A comparative analysis study is conducted using three classification datasets. Results demonstrate that the proposed AFI method can not only improve the accuracy of Micro-EBRBS, but also reduce the number of failed data in the process of rule inference. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-88601-1_8 | BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2021) |
Keywords | DocType | Volume |
Extended belief rule-based system, Activation factor, Classification, Rule inference | Conference | 12915 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long-Hao Yang | 1 | 0 | 1.01 |
| Jun Liu | 2 | 644 | 56.21 |
| Ying-Ming Wang | 3 | 3256 | 166.96 |
| Hui Wang | 4 | 185 | 14.52 |
| Luis Martínez | 5 | 2667 | 109.59 |