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- W2808833850 abstract "Missing values are common in real-world data and may seriously affect data analytics such as simple statistics and hypothesis testing. Generally speaking, there are two types of missing values: explicitly missing values (i.e. NULL values), and implicitly missing values (a.k.a. disguised missing values (DMVs)) such as 11111111 for a phone number and Some college for education. While detecting explicitly missing values is trivial, detecting DMVs is not; the essential challenge is the lack of standardization about how DMVs are generated. In this paper, we present FAHES, a robust system for detecting DMVs from two angles: DMVs as detectable outliers and as detectable inliers. For DMVs as outliers, we propose a syntactic outlier detection module for categorical data, and a density-based outlier detection module for numerical values. For DMVs as inliers, we propose a method that detects DMVs which follow either missing-completely-at-random or missing-at-random models. The robustness of FAHES is achieved through an ensemble technique that is inspired by outlier ensembles. Our extensive experiments using real-world data sets show that FAHES delivers better results than existing solutions." @default.
- W2808833850 created "2018-06-29" @default.
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- W2808833850 date "2018-07-19" @default.
- W2808833850 modified "2023-10-18" @default.
- W2808833850 title "FAHES" @default.
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- W2808833850 doi "https://doi.org/10.1145/3219819.3220109" @default.
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