Name
Abstract | ||
|---|---|---|
Abstract—Many studies on machine learning, and more specif- ically on regression, focus on the search for a precise model, when precise data are available. Therefore, it is well-known that the model,thus found,may,not exactly describe the target concept, due to the existence of learning bias. In order to overcome the problem of too much illusionary precise models, this paper provides a general framework,for imprecise regression from,non-fuzzy input and,output,data. The goal of imprecise regression is to find a model,that has the better tradeoff between faithfulness w.r.t. data and (meaningful) precision. We propose,an algorithm,based on simulated,annealing,for linear and non-linear imprecise,regression with triangular and,trapezoidal,fuzzy sets. This approach,is compared,with the different fuzzy regression frameworks, especially with possibilistic regression. Experiments on an environmental,database,show,promising,results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FUZZY.2007.4295605 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy set theory,regression analysis,simulated annealing,fuzzy regression,fuzzy sets,imprecise regression,machine learning,possibilistic regression,simulated annealing | Data mining,Fuzzy classification,Defuzzification,Computer science,Regression analysis,Fuzzy set operations,Nonparametric regression,Proper linear model,Fuzzy set,Artificial intelligence,Fuzzy number,Machine learning | Conference |
ISSN | Citations | PageRank |
1098-7584 | 4 | 0.59 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Serrurier | 1 | 267 | 26.94 |
| Henri Prade | 2 | 10549 | 1445.02 |