Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An efficient ensemble method for classifying skewed data streams
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
| Hi-index | 0.00 |
The data streams in various real life applications are characterized by concept drift. Such data streams may also be characterized by skewed or imbalance class distributions for example Financial fraud detection, Network intrusion detection etc. In such cases skewed class distribution of the stream increases the problems associated with classifying stream instances. Learning from such skewed data streams results in a classifier which is biased towards the majority class. Thus the classifier built on such skewed data streams tends to misclassify the minority class examples. In case of some applications like financial fraud detection the identification of fraudulent transaction is the main focus because here misclassification of such minority class instances will result in financial loss. Similarly in case of many other real life data stream applications the misclassification costs associated with minority class instances are higher and they need proactive treatment. In this paper we present our preliminary work where in we propose a method which makes use of k nearest neighbours and oversampling technique to balance the class distributions. Experimental results show that the approach shows good classification performance on synthetic and real world data sets.