Next Article in Journal
Nonclassical Symmetries of a Power Law Harry Dym Equation
Previous Article in Journal
The Breaking of Symmetry Leads to Chirality in Cucurbituril-Type Hosts
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(4), 99; https://doi.org/10.3390/sym10040099

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

1
Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, 190002 Popayán, Colombia
2
Departamento de Ciencias de la Computación e Ingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, Spain
3
Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, 190002 Popayán, Colombia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 9 March 2018 / Revised: 28 March 2018 / Accepted: 30 March 2018 / Published: 6 April 2018
View Full-Text   |   Download PDF [1159 KB, uploaded 3 May 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top