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Open AccessArticle

How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

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Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, 190002 Popayán, Colombia
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Departamento de Ciencias de la Computación e Ingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, Spain
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Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, 190002 Popayán, Colombia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2018, 10(4), 99; https://doi.org/10.3390/sym10040099
Received: 9 March 2018 / Revised: 28 March 2018 / Accepted: 30 March 2018 / Published: 6 April 2018
Today, data availability has gone from scarce to superabundant. Technologies like IoT, trends in social media and the capabilities of smart-phones are producing and digitizing lots of data that was previously unavailable. This massive increase of data creates opportunities to gain new business models, but also demands new techniques and methods of data quality in knowledge discovery, especially when the data comes from different sources (e.g., sensors, social networks, cameras, etc.). The data quality process of the data set proposes conclusions about the information they contain. This is increasingly done with the aid of data cleaning approaches. Therefore, guaranteeing a high data quality is considered as the primary goal of the data scientist. In this paper, we propose a process for data cleaning in regression models (DC-RM). The proposed data cleaning process is evaluated through a real datasets coming from the UCI Repository of Machine Learning Databases. With the aim of assessing the data cleaning process, the dataset that is cleaned by DC-RM was used to train the same regression models proposed by the authors of UCI datasets. The results achieved by the trained models with the dataset produced by DC-RM are better than or equal to that presented by the datasets’ authors. View Full-Text
Keywords: data cleaning in regression models (DC-RM); data quality issue; data cleaning task; regression model data cleaning in regression models (DC-RM); data quality issue; data cleaning task; regression model
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Corrales, D.C.; Corrales, J.C.; Ledezma, A. How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning. Symmetry 2018, 10, 99.

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