Abstract
Although they can learn from raw data, many concept learning algorithms require that the training data contain only discrete data. However, real world problems contain, more often than not, both numeric and discrete data. So before these algorithms can be applied, data discretization (quantization) is needed. This paper introduces X2R, a simple and fast algorithm that can be applied to both numeric and discrete data, and generate rules from datasets like Season-Classification, Golf-Playing that contain continuous and/or discrete data. The empirical results demonstrate that X2R can effectively generate rules from the raw data and perform better than some of its peers in terms of the quality of rules and time complexities.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Editors | Anon |
| Publisher | IEEE |
| Pages | 1631-1635 |
| Number of pages | 5 |
| Volume | 2 |
| State | Published - 1995 |
| Externally published | Yes |
| Event | Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) - Vancouver, BC, Can Duration: Oct 22 1995 → Oct 25 1995 |
Other
| Other | Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) |
|---|---|
| City | Vancouver, BC, Can |
| Period | 10/22/95 → 10/25/95 |
ASJC Scopus subject areas
- Hardware and Architecture
- Control and Systems Engineering