淡江大學機構典藏:Item 987654321/107454
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    题名: Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition
    作者: Yu-Xiang Zhao;Chien-Hsing Chou
    关键词: feature selection;neighborhood relationship;EEG signal;Chinese character recognition
    日期: 2016-06-14
    上传时间: 2016-08-19 18:42:31 (UTC+8)
    出版者: MDPI AG
    摘要: : In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters.
    關聯: Sensors 16(6), p.871(15 pages)
    DOI: 10.3390/s16060871
    显示于类别:[電機工程學系暨研究所] 期刊論文

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