id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_oupt5hnjxjdyteqxbkcurzltsm	Chao-Ton Su	Knowledge acquisition through information granulation for imbalanced data	2006	11	.pdf	application/pdf	7533	1162	70	This paper proposes a novel method called 'knowledge acquisition via information granulation' (KAIG) model which not only can remove some unnecessary details and provide a better insight into the essence of data but also effectively solve 'class imbalance' problems. UCI data bank, including one imbalanced diagnosis data (pima-Indians-diabetes), are provided to evaluate the effectiveness of KAIG model. using different performance indexes, overall accuracy, G-mean and Receiver Operation Characteristic (ROC) curve, the experimental results Keywords: Information granulation; Fuzzy ART; Granular computing; Knowledge acquisition; Imbalanced data Sampling methods reduce data imbalance—by 'downsampling' (removing) instances from majority class or 'upsampling' (duplicating) the training instances from the construct IGs by the similarity of numerical data, the amount of knowledge acquisition via information granulation (KAIG) model. The rough sets method can be utilized to remove superfluous sub-attributes and to acquire knowledge. Numerical data (similarityZ1.0) Traditional methods Granules (similarityZ0.85) KAIG Knowledge acquisition through information granulation for imbalanced data	./cache/work_oupt5hnjxjdyteqxbkcurzltsm.pdf	./txt/work_oupt5hnjxjdyteqxbkcurzltsm.txt