SPFCNN-Miner: A new classifier to tackle class-unbalanced data

Researchers at Chongqing University in China have recently developed a cost-sensitive meta-learning classifier that can be used when the training data available is high-dimensional or limited. Their classifier, called SPFCNN-Miner, was presented in a paper published in Elsevier's Future Generation Computer Systems.

from News on Artificial Intelligence and Machine Learning http://bit.ly/2KxUDbh

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