Abstract
One of the most difficult components for multi-class classification system is to find an appropriate error-correcting output codes (ECOC) matrix, which is used to decompose the multi-class problem into several binary class problems. In this paper, an indicator based multi-objective evolutionary algorithm with preference involved is designed to search the high-quality ECOC matrix. Specifically, the Harrington's one-sided desirability function is integrated into an indicator-based evolutionary algorithm (IBEA), which aims to approximate the relevant regions of pareto front (PF) according to the preference of the decision maker. Simulation results show that the proposed approach has better classification performance than compared multi-class based algorithms
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2014 International Conference on Machine Learning and Cybernetics |
| Publisher | IEEE |
| Pages | 147-152 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479942152 |
| ISBN (Print) | 9781479942169 |
| DOIs | |
| Publication status | Published - Jul 2014 |
| Externally published | Yes |
| Event | 2014 International Conference on Machine Learning and Cybernetics - Crowne Plaza Lanzhou, Lanzhou, China Duration: 13 Jul 2014 → 16 Jul 2014 |
Conference
| Conference | 2014 International Conference on Machine Learning and Cybernetics |
|---|---|
| Country/Territory | China |
| City | Lanzhou |
| Period | 13/07/14 → 16/07/14 |
Keywords
- Error-correcting output coding
- Harrington's one-sided desirability function
- Indicator-based evolutionary algorithm
- Multi-class problem
- Pareto front