Name
Abstract | ||
|---|---|---|
Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate data. Many of these use non-linear models to handle complex data-relationships in the input data. Their lack of transparency, however, is problematic since comprehensibility is a key requirement in many potential application domains. Rule-extraction algorithms have been proposed to solve this problem for classification by extracting comprehensible rule sets from the often better performing, complex models. We present a new pedagogical rule extraction algorithm for regression, based on active learning, which can be combined with any existing rule induction technique. Empirical results show that the proposed ALPA-R rule extraction method improves on classical rule induction techniques, both in accuracy and fidelity. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDMW.2012.13 | ICDM Workshops |
Keywords | Field | DocType |
data mining,input data,approximate data,rule extraction,complex data-relationships,new pedagogical rule extraction,comprehensible rule set,classical rule induction technique,accurate regression model,proposed alpa-r rule extraction,existing rule induction technique,regression analysis,learning artificial intelligence,data handling,approximation theory,active learning | Data mining,Fidelity,Active learning,Regression,Extraction algorithm,Computer science,Regression analysis,Approximation theory,Artificial intelligence,Rule induction,Group method of data handling,Machine learning | Conference |
ISSN | Citations | PageRank |
2375-9232 | 4 | 0.40 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enric Junque de Fortuny | 1 | 4 | 1.07 |
| David Martens | 2 | 66 | 9.52 |