Name
Abstract | ||
|---|---|---|
Ordinal classification problems with monotonicity constraints (also referred to as multicriteria classification problems) often appear in real-life applications, however, they are considered relatively less frequently in theoretical studies than regular classification problems. We introduce a rule induction algorithm based on the statistical learning approach that is tailored for this type of problems. The algorithm first monotonizes the dataset (excludes strongly inconsistent objects), using Stochastic Dominance-based Rough Set Approach, and then uses forward stagewise additive modeling framework for generating a monotone rule ensemble. Experimental results indicate that taking into account knowledge about order andmonotonicity constraints in the classifier can improve the prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3233/FI-2009-124 | Fundam. Inform. |
Keywords | Field | DocType |
rough set,additive model,boosting,stochastic dominance | Ordinal number,Stochastic dominance,Rough set,Learning rule,Multicriteria classification,Boosting (machine learning),Artificial intelligence,Rule induction,Dominance-based rough set approach,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
94 | 2 | 0169-2968 |
Citations | PageRank | References |
17 | 0.67 | 22 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krzysztof Dembczynski | 1 | 41 | 3.30 |
| Wojciech Kotlowski | 2 | 158 | 16.32 |
| Roman Slowinski | 3 | 5561 | 516.06 |
| DembczyńskiKrzysztof | 4 | 17 | 0.67 |
| KotłowskiWojciech | 5 | 17 | 0.67 |
| SłowińskiRoman | 6 | 17 | 0.67 |