The Rule Induction from a Set of Exemplars classifier (RISE) was introduced by Pedro Domingos from University of California in 1996 [1]. The most innovative feature of this algorithm is that it combines rule induction with instance-based learning, in the attempt of building a more accurate classifier than previous rule-based approaches.
The objective of this work is the implementation and evaluation of a custom Python version of RISE for classification tasks, applying it to 4 data sets of different sizes and features. The accuracy of each trained model is computed, as well as the coverage and local accuracy of each rule.
[1] Domingos, P. (1996). Unifying instance-based and rule-based induction. Machine Learning, 24(2), 141-168.
| Author | Albert Espín (except datasets, gathered from UCI's Machine Learning repository) |
| Date | March 2019 |
| Code license | MIT |
| Report license | Creative Commons Attribution, Non-Commercial, Non-Derivative |
| Dataset licence | Licenses specified for each dataset in UCI's Machine Learning repository |
