A Masking Index for Quantifying Hidden Glitches

Conference object, Article, Other literature type English OPEN
Berti-Équille , Laure; Loh , Ji Meng; Dasu , Tamraparni;
  • Publisher: IEEE
  • Related identifiers: doi: 10.1109/ICDM.2013.16, doi: 10.1007/s10115-014-0760-0
  • Subject: [INFO.INFO-WB]Computer Science [cs]/Web | Masking | [STAT.OT]Statistics [stat]/Other Statistics [stat.ML] | [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] | [ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG] | [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] | [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML] | [ INFO.INFO-WB ] Computer Science [cs]/Web | Anomaly detection | Outlier detection | [ STAT.OT ] Statistics [stat]/Other Statistics [stat.ML] | [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB] | [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI] | [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] | [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] | Duplicate record identification | Missing values

Data glitches are errors in a dataset. They are complex entities that often span multiple attributes and records. When they co-occur in data, the presence of one type of glitch can hinder the detection of another type of glitch. This phenomenon is called masking. In thi... View more
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