Introduction to the theory of neural computation
Introduction to the theory of neural computation
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining and Knowledge Discovery
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Classifying imbalanced data using a bagging ensemble variation (BEV)
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
The class imbalance problem: A systematic study
Intelligent Data Analysis
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Imbalanced text classification: A term weighting approach
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
When using Neural Networks (NN) to handle class imbalance problems, there exists a fact that minority class makes less contribution to the error function than the majority class, so the network learned prefers to recognizing majority class data which we pay less attention to. This paper proposes a novel algorithm WNN (Weighted NN) to solve this problem using a newly defined error function in BP (BP) algorithm. Experimental results executed on 20 UCI datasets show that the approach can effectively enhance the recognition rate of minority class data.