Search Results - subject:"missing value"

Data cleaning issues in class imbalanced datasets: instance selection and missing values imputation for one-class classifiers

Wang, Zhenyuan; Tsai, Chih-Fong; Lin, Wei-Chao - In: Data Technologies and Applications 55 (2021) 5, pp. 771-787

performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter … missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance … values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain …

Persistent link: https://www.econbiz.de/10014712610

A systematic review of machine learning-based missing value imputation techniques

Thomas, Tressy; Rajabi, Enayat - In: Data Technologies and Applications 55 (2021) 4, pp. 558-585

electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2 …

Persistent link: https://www.econbiz.de/10014712797

Innovation and entrepreneurship : the necessary conditions of value creation

Barnard, Brian; Herbst, Derrick - In: Expert journal of business and management 5 (2017) 2, pp. 98-128

Persistent link: https://www.econbiz.de/10011749767

Methodological challenges in observational studies : measuring socio-economic status and alcohol consumption

Grittner, Ulrike - 2016

Persistent link: https://www.econbiz.de/10012239936

Combining Data Preprocessing Methods With Imputation Techniques for Software Defect Prediction

Kakkar, Misha; Jain, Sarika; Bansal, Abhay; Grover, P.S. - In: International Journal of Open Source Software and … 9 (2018) 1, pp. 1-19

Software Defect Prediction (SDP) models are used to predict, whether software is clean or buggy using the historical data collected from various software repositories. The data collected from such repositories may contain some missing values. In order to estimate missing values, imputation...

Persistent link: https://www.econbiz.de/10012046987

Product failure prediction with missing data

Kang, Seokho; Kim, Eunji; Shim, Jaewoong; Chang, Wonsang; … - In: International journal of production research 56 (2018) 14, pp. 4849-4859

Persistent link: https://www.econbiz.de/10011925126

Recognition performance of imputed control chart patterns using exponentially weighted moving average

Razieh Haghighati; Adnan Hassan - In: European journal of industrial engineering : EJIE 12 (2018) 5, pp. 637-660

Persistent link: https://www.econbiz.de/10011977985

Prediction Length of Stay with Neural Network Trained by Particle Swarm Optimization

Oliyaei, Azadeh; Aghababaee, Zahra - In: International Journal of Big Data and Analytics in … 2 (2017) 2, pp. 21-38

This article describes how the prediction of the length of stay demonstrates the severity of the disease as well as the practice patterns of hospitals. Also, it helps the hospital resources management provide better services for inpatients and increase inpatients' satisfaction. In this article,...

Persistent link: https://www.econbiz.de/10012043251

Random aggregation with applications in high‐frequency finance

Tsay, Ruey S.; Jin‐Huei Yeh - In: Journal of Forecasting 30 (2011) 1, pp. 72-103

In this paper we consider properties of random aggregation in time series analysis. For application, we focus on the problem of estimating the high-frequency beta of an asset return when the returns are subject to the effects of market microstructure. Specifically, we study the correlation...

Persistent link: https://www.econbiz.de/10008774206

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

Gheyas, Iffat A. - 2009

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of...

Persistent link: https://www.econbiz.de/10009465839