南京航空航天大学k8凯发集团主页平台管理系统 皮德常-k8凯发集团

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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