淡江大學機構典藏:Item 987654321/107454
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/107454


    Title: Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition
    Authors: Yu-Xiang Zhao;Chien-Hsing Chou
    Keywords: feature selection;neighborhood relationship;EEG signal;Chinese character recognition
    Date: 2016-06-14
    Issue Date: 2016-08-19 18:42:31 (UTC+8)
    Publisher: MDPI AG
    Abstract: : 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.
    Relation: Sensors 16(6), p.871(15 pages)
    DOI: 10.3390/s16060871
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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