Neighbourhood sampling in bagging for imbalanced data
نویسندگان
چکیده
منابع مشابه
Neighbourhood sampling in bagging for imbalanced data
Various approaches to extend bagging ensembles for class imbalanced data are considered. First, we review known extensions and compare them in a comprehensive experimental study. The results show that integrating bagging with under-sampling is more powerful than over-sampling. They also allow to distinguish Roughly Balanced Bagging as the most accurate extension. Then, we point out that complex...
متن کاملExtending Bagging for Imbalanced Data
Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside its neighbourhood. Experiments indicate that ...
متن کاملRoughly Balanced Bagging for Imbalanced Data
Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distributions. In our new sampling method “Roughly Balanced Bagging” (RB Bagging), the number of samples in the largest and smallest classes are different, but they are effectively balanced when averaged over all subsets, wh...
متن کاملActively Balanced Bagging for Imbalanced Data
Under-sampling extensions of bagging are currently the most accurate ensembles specialized for class imbalanced data. Nevertheless, since improvements of recognition of the minority class, in this type of ensembles, are usually associated with a decrease of recognition of majority classes, we introduce a new, two phase, ensemble called Actively Balanced Bagging. The proposal is to first learn a...
متن کاملApplicability of Roughly Balanced Bagging for Complex Imbalanced Data
Roughly Balanced Bagging is based on under-sampling and classifies imbalanced data much better than other ensembles. In this paper, we experimentally study its properties that may influence its good performance. Results of experiments show that it can be constructed with a small number of component classifiers, which are quite accurate, however, of low diversity. Moreover, its good performance ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2015
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2014.07.064