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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：SIAM Int. Conf. Data Min., SDM
摘要：Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fréchet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold δ. Experimental results demonstrate the algorithm effectiveness compared with the state-of the-art time series selection algorithms on real-world EEG datasets. © 2018 by SIAM.
是否译文：否
发表时间：2018-01-01
合写作者：代成龙,吴佳,崔琳
通讯作者：皮德常