Name
Abstract | ||
|---|---|---|
In feature selection, the number of selected features and the classification accuracy are two common objectives to be optimized. However, few studies pay attention to which features are selected. In many feature selection problems, different feature subsets with the same number of selected features can achieve similar classification accuracy. These are multimodal multiobjective optimization (MMO) problems in feature selection. In this paper, the MMO problems in feature selection are described in detail. Then, the great significance and importance to find these different feature subsets are discussed. Two modified MMO algorithms are used to solve the MMO feature selection problems. Simulation results show that these MMO algorithms can find more feature subsets than unimodal optimization algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CEC.2019.8790329 | 2019 IEEE Congress on Evolutionary Computation (CEC) |
Keywords | Field | DocType |
Multimodal multiobjective optimization,feature selection,particle swarm optimization | Feature selection,Computer science,Evolutionary computation,Feature extraction,Multi-objective optimization,Optimization algorithm,Artificial intelligence,Statistical classification,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-2154-3 | 2 | 0.36 |
References | Authors | |
21 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. T. Yue | 1 | 2 | 0.36 |
| Jing J. Liang | 2 | 2073 | 107.92 |
| Bo-Yang Qu | 3 | 1215 | 46.32 |
| K. J. Yu | 4 | 2 | 0.36 |
| H. Song | 5 | 2 | 0.36 |