|
|
|
|
|
Menna Ibrahim Gabr, Yehia Mostafa Helmy and Doaa Saad Elzanfaly
Data completeness is one of the most common challenges that hinder the performance of data analytics platforms. Different studies have assessed the effect of missing values on different classification models based on a single evaluation metric, namely, a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Edgar Acuna, Roxana Aparicio and Velcy Palomino
In this paper we investigate the effect of two preprocessing techniques, data imputation and smoothing, in the prediction of blood glucose level in type 1 diabetes patients, using a novel deep learning model called Transformer. We train three models: XGB...
ver más
|
|
|
|
|
|
|
|
|
|
|
Muhammad Shoaib Arif, Aiman Mukheimer and Daniyal Asif
Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Andrés F. Ochoa-Muñoz and Javier E. Contreras-Reyes
Missing or unavailable data (NA) in multivariate data analysis is often treated with imputation methods and, in some cases, records containing NA are eliminated, leading to the loss of information. This paper addresses the problem of NA in multiple facto...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O?Bryant and on behalf of the Health and Aging Brain Study (HABS?HD) Study Team
The Health and Aging Brain Study?Health Disparities (HABS?HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS?HD is missing data. It is impossible to...
ver más
|
|
|
|
|
|
|
|
|
|
|
Benjamin Agbo, Hussain Al-Aqrabi, Richard Hill and Tariq Alsboui
The Internet of Things (IoT) has had a tremendous impact on the evolution and adoption of information and communication technology. In the modern world, data are generated by individuals and collected automatically by physical objects that are fitted wit...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sergio Arciniegas-Alarcón, Marisol García-Peña, Camilo Rengifo and Wojtek J. Krzanowski
We describe imputation strategies resistant to outliers, through modifications of the simple imputation method proposed by Krzanowski and assess their performance. The strategies use a robust singular value decomposition, do not depend on distributional ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Qinggang Gao, Joseph Molloy and Kay W. Axhausen
We studied trip purpose imputation using data mining and machine learning techniques based on a dataset of GPS-based trajectories gathered in Switzerland. With a large number of labeled activities in eight categories, we explored location information usi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Laura Sofía Hoyos-Gomez and Belizza Janet Ruiz-Mendoza
Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some loc...
ver más
|
|
|
|
|
|
|
|
|
|
|
Luca Cappelletti, Tommaso Fontana, Guido Walter Di Donato, Lorenzo Di Tucci, Elena Casiraghi and Giorgio Valentini
Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieve...
ver más
|
|
|
|
|
|