Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
نویسندگان
چکیده
منابع مشابه
A Novel Approach for Handling Imbalanced Data in Medical Diagnosis using Undersampling Technique
In many data mining applications the imbalanced learning problem is becoming ubiquitous nowadays. When the data sets have an unequal distribution of samples among classes, then these data sets are known as imbalanced data sets. When such highly imbalanced data sets are given to any classifier, then classifier may misclassify the rare samples from the minority class. To deal with such type of im...
متن کاملAddressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling
In the classification framework there are problems in which the number of examples per class is not equitably distributed, formerly known as imbalanced data sets. This situation is a handicap when trying to identify the minority classes, as the learning algorithms are not usually adapted to such characteristics. An usual approach to deal with the problem of imbalanced data sets is the use of a ...
متن کاملNeighbourhood 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 کاملAn Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets
Most classifiers work well when the class distribution in the response variable of the dataset is well balanced. Problems arise when the dataset is imbalanced. This paper applied four methods: Oversampling, Undersampling, Bagging and Boosting in handling imbalanced datasets. The cardiac surgery dataset has a binary response variable (1=Died, 0=Alive). The sample size is 4976 cases with 4.2% (Di...
متن کاملLearning from Imbalanced Data Using Ensemble Methods and Cluster-Based Undersampling
Imbalanced data, where the number of instances of one class is much higher than the others, are frequent in many domains such as fraud detection, telecommunications management, oil spill detection and text classification. Traditional classifiers do not perform well when considering data that are susceptible to both within-class and between-class imbalances. In this paper, we propose the ClustFi...
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: Information Sciences
سال: 2020
ISSN: 0020-0255
DOI: 10.1016/j.ins.2019.08.062