Resampling-Based Ensemble Methods for Online Class Imbalance Learning
نویسندگان
چکیده
منابع مشابه
Ensemble diversity for class imbalance learning
This thesis studies the diversity issue of classification ensembles for class imbalance learning problems. Class imbalance learning refers to learning from imbalanced data sets, in which some classes of examples (minority) are highly under-represented comparing to other classes (majority). The very skewed class distribution degrades the learning ability of many traditional machine learning meth...
متن کاملEnsemble-based active learning for class imbalance problem
In medical diagnosis, the problem of class imbalance is popular. Though there are abundant unlabeled data, it is very difficult and expensive to get labeled ones. In this paper, an ensemble-based active learning algorithm is proposed to address the class imbalance problem. The artificial data are created according to the distribution of the training dataset to make the ensemble diverse, and the...
متن کاملEnsemble-Based Wrapper Methods for Feature Selection and Class Imbalance Learning
The wrapper feature selection approach is useful in identifying informative feature subsets from high-dimensional datasets. Typically, an inductive algorithm “wrapped” in a search algorithm is used to evaluate the merit of the selected features. However, significant bias may be introduced when dealing with highly imbalanced dataset. That is, the selected features may favour one class while bein...
متن کاملEnsemble of subset online sequential extreme learning machine for class imbalance and concept drift
In this paper, a computationally efficient framework, referred to as ensemble of subset online sequential extreme learning machine (ESOS-ELM), is proposed for class imbalance learning from a concept-drifting data stream. The proposed framework comprises a main ensemble representing short-term memory, an information storage module representing long-term memory and a change detection mechanism to...
متن کاملClass Imbalance Learning Methods for Support Vector Machines
Support Vector Machines is a very popular machine learning technique. Despite of all its theoretical and practical advantages, SVMs could produce suboptimal results with imbalanced datasets. That is, an SVM classifier trained on an imbalanced dataset can produce suboptimal models which are biased towards the majority class and have low performance on the minority class, like most of the other c...
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: IEEE Transactions on Knowledge and Data Engineering
سال: 2015
ISSN: 1041-4347
DOI: 10.1109/tkde.2014.2345380