| Title |
|
|
|
Active learning-based rule extraction for regression
| |
| Author |
|
|
|
| |
| Abstract |
|
|
|
| Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate datasets. 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 rulesets 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. | | |
| Language |
|
|
|
English
| |
| Source (book) |
|
|
|
2012 IEEE 12th International Conference on Data Mining Workshops, Brussels (Belgium) | |
| Publication |
|
|
|
S.l. : IEEE, 2012
| |
| ISBN |
|
|
|
978-0-7695-4925-5
| |
| Volume/pages |
|
|
|
(2012), p. 926-933
| |
| ISI |
|
|
|
000320946500134
| |
| Full text (Publisher's DOI) |
|
|
| | |
|