教師資料查詢 | 類別: 期刊論文 | 教師: 許輝煌 Hsu Hui-huang (瀏覽個人網頁)

標題:KNN-DTW Based Missing Value Imputation for Microarray Time Series Data
學年99
學期2
出版(發表)日期2011/03/01
作品名稱KNN-DTW Based Missing Value Imputation for Microarray Time Series Data
作品名稱(其他語言)
著者Hsu, Hui-Huang; Yang, Andy C.; Lu, Ming-Da
單位淡江大學資訊工程學系
出版者Oulu: Academy Publisher
著錄名稱、卷期、頁數Journal of Computers 6(3), pp.418-425
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.
關鍵字Microarray Time Series Data;Missing Value Imputation;Dynamic Time Warping;K-Nearest Neighbor
語言英文
ISSN1796-203X
期刊性質國外
收錄於EI;
產學合作
通訊作者
審稿制度
國別芬蘭
公開徵稿
出版型式,紙本
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