Sciweavers

Share
FLAIRS
2008

Selecting Minority Examples from Misclassified Data for Over-Sampling

11 years 2 months ago
Selecting Minority Examples from Misclassified Data for Over-Sampling
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method selects minority examples from misclassified data given by an ensemble of classifiers. Then, these instances are over-sampled to create new synthetic examples using a variant of the well-known SMOTE algorithm. To build the ensemble we use the bagging method and locally weighted linear regression as the machine learning algorithm. We tested our method using several data sets from the UCI machine learning repository. Our experimental results show that our approach obtains very good results, in fact it showed better recall and precision than SMOTE.
Jorge de la Calleja, Olac Fuentes, Jesús Go
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where FLAIRS
Authors Jorge de la Calleja, Olac Fuentes, Jesús González
Comments (0)
books