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FADE: Focused and Attention-Based Detector of Errors

Guardado en:
Publicado en:Journal of Database Management vol. 36, no. 1 (2025), p. 1-27
Autor principal: Miller, James
Otros Autores: Al-Shamali, Omar, Quader, Shaikh
Publicado:
IGI Global
Materias:
Quantitative analysis
Data analysis
Datasets
Errors
Classification
Error detection
Qualitative analysis
Machine learning
Marketing
Garbage
Artificial intelligence
Wastes
Error correction & detection
Error analysis
Attention
Neural networks
Sensors
Structured data
Data
Attributes
Acceso en línea:Citation/Abstract
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Resumen:Error detection is an important part of preparing data for data analysis. Erroneous data can result in inaccurate analysis, resulting in garbage-in, garbage-out. Currently, many models utilize either or both Qualitative and Quantitative methods to detect errors in the data. However, these methods are still limited in the errors they can detect. Hence FADE, Focused and Attention-based Detector of Errors, was proposed. FADE can detect errors within structured data with rows and columns. FADE utilizes the information found in surrounding cells within the same row to help determine if a cell is erroneous. It also learns the expected structure of the attributes in the dataset and the values expected in each attribute. This results in FADE having a much wider range of error type detection and having a higher classification of errors than other methods. FADE was evaluated and was found to detect these errors with relatively high performance.
ISSN:1063-8016
1533-8010
1047-9430
DOI:10.4018/JDM.377526
Fuente:ABI/INFORM Global