Name
Abstract | ||
|---|---|---|
One of the most important issues for the development of classification tasks is how to design a powerful classifier with strong generalization capability. Multi-functional nearest-neighbor (MFNN) has recently proven to generalize the different versions of nearest-neighbor classifier by choosing appropriate similarity measures and aggregators, and has been demonstrated to enhance the more comprehensive adaptivity and higher accuracy. In this paper, the multiple kernel learning-based multi-functional nearest-neighbor classification approach (MMNN) is proposed to improve the performance of MFNN. In terms of the superiority that kernel-based fuzzy rough set have, kernel functions can be used to compute fuzzy similarity relations. MMNN is a reformative framework to combine a particular multiple kernel learning method with a flexible nearest-neighbor classifier named MFNN. Meanwhile, the results of experiment show that the proposed method outperform other state of the art algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICACI.2018.8377561 | 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
Multiple kernel learning,Fuzzy-rough sets,Nearest-neighbor classification | k-nearest neighbors algorithm,Kernel (linear algebra),Task analysis,Pattern recognition,Computer science,Multiple kernel learning,Rough set,Fuzzy set,Artificial intelligence,Classifier (linguistics),Kernel (statistics) | Conference |
ISBN | Citations | PageRank |
978-1-5386-4363-1 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingshan Zhao | 1 | 0 | 0.34 |
| Yanpeng Qu | 2 | 29 | 7.46 |
| Ansheng Deng | 3 | 3 | 2.73 |