In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine ...
The classification of remote sensing data with imbalanced training data is addressed. The classification accuracy of a supervised method is affected by several factors, such as the classifier algorithm, the input data and the available training data. ...
Many current malware detection methods are based on supervised learning techniques, which however have certain limitations. First, these techniques require a large amount of labeled data for training which is often difficult to obtain. Second, ...
Kluwer Academic Publishers
United States
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