Data Selection Based on Fuzzy Clustering
نویسندگان
چکیده
When the number of training data is limited, the performance of supervised learning could be improved if valuable samples are selected for training. In this work, we propose a novel data selection method based on fuzzy clustering. Our method first partitions all the data which need to be classified into clusters. Then training data are selected from each cluster based on their membership degrees. Experimental results show that our proposed fuzzy clustering-based data selection method could effectively improve the performance of learning compared with randomly selecting training samples.
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