Journal
APPLIED INTELLIGENCE
Volume 35, Issue 1, Pages 123-133Publisher
SPRINGER
DOI: 10.1007/s10489-009-0207-6
Keywords
kNN; Shell-NN; Missing data imputation; Mining incomplete data
Categories
Funding
- Australian Research Council (ARC) [DP0985456]
- Nature Science Foundation (NSF) of China [90718020]
- China 973 Program [2008CB317108]
- China Ministry of Personnel for Overseas-Return High-level Talents
- MOE
- Social Sciences at Universities [07JJD720044]
- Guangxi NSF
- Australian Research Council [DP0985456] Funding Source: Australian Research Council
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Data preparation is an important step in mining incomplete data. To deal with this problem, this paper introduces a new imputation approach called SN (Shell Neighbors) imputation, or simply SNI. The SNI fills in an incomplete instance (with missing values) in a given dataset by only using its left and right nearest neighbors with respect to each factor (attribute), referred them to Shell Neighbors. The left and right nearest neighbors are selected from a set of nearest neighbors of the incomplete instance. The size of the sets of the nearest neighbors is determined with the cross-validation method. And then the SNI is generalized to deal with missing data in datasets with mixed attributes, for example, continuous and categorical attributes. Some experiments are conducted for evaluating the proposed approach, and demonstrate that the generalized SNI method outperforms the kNN imputation method at imputation accuracy and classification accuracy.
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