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Agricultural research has been profited by technical advances such as automation, data mining. Today ,data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively a young research field. The large(More)
Many real world data sets have an imbalanced distribution of the instances. Learning from such data sets results in the classifier being biased towards the majority class, thereby tending to misclassify the minority class samples. In this paper, we provide a technique, SkewBoost which classifies the minority instances correctly without compromising much on(More)
One of fundamental problem in the task of mining streaming data is the concept drift over time. Such data Streams may also exhibit high and varying degrees of class imbalance, which can further complicate the task. In scenarios like these, class imbalance is particularly difficult to overcome and has not been as thoroughly studied. Most of the studies on(More)
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(More)