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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55114

    題名: KNN-DTW Based Missing Value Imputation for Microarray Time Series Data
    作者: Hsu, Hui-Huang;Yang, Andy C.;Lu, Ming-Da
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Microarray Time Series Data;Missing Value Imputation;Dynamic Time Warping;K-Nearest Neighbor
    日期: 2011-03
    上傳時間: 2011-08-16 16:05:41 (UTC+8)
    出版者: Oulu: Academy Publisher
    摘要: Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene array values. Several algorithms are proposed to handle this problem, but they have various limitations. In this paper, we develop a novel method to impute missing values in microarray time-series data combining k-nearest neighbor (KNN) and dynamic time warping (DTW). We also analyze and implement several variants of DTW to further improve the efficiency and accuracy of our method. Experimental results show that our method is more accurate compared with existing missing value imputation methods on real microarray time series datasets.
    關聯: Journal of Computers 6(3), pp.418-425
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


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