Results 221 to 230 of about 178,240 (253)

A hybrid method for missing value imputation

Proceedings of the 23rd Pan-Hellenic Conference on Informatics, 2019
Missing values are a common incurrence in a great number of real-world datasets, emerging from diverse domains of interest. In research, missing data constitute a significant problem as it can affect the conclusions drawn from them. Considering this, the difficulty of data preprocessing is increasing as selecting an inappropriate way to handle missing ...
Aikaterini Karanikola, Sotiris Kotsiantis   +1 more
openaire   +1 more source

A Hybrid Method for Incomplete Data Imputation

2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, 2015
With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI).
Liang Zhao 0005, Zhikui Chen, Zhennan Yang, Yueming Hu 0001   +3 more
openaire   +1 more source

GBKII: An Imputation Method for Missing Values

2007
Missing data imputation is an actual and challenging issue in machine learning and data mining. This is because missing values in a dataset can generate bias that affects the quality of the learned patterns or the classification performances. To deal with this issue, this paper proposes a Grey-Based K-NN Iteration Imputation method, called GBKII, for ...
Chengqi Zhang, Xiaofeng Zhu 0001, Jilian Zhang, Yongsong Qin, Shichao Zhang 0001   +4 more
openaire   +1 more source

Imputation methods for incomplete data

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015
Although sometimes encounter data sets that contain one or more missing feature values (incomplete data). Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements.
Vaishali H. Umathe, Gauri Chaudhary
openaire   +1 more source

Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data

Briefings in Bioinformatics, 2022
Sandra Taylor, Kyoungmi Kim, Machelle Wilson   +2 more
exaly  

Deep learning versus conventional methods for missing data imputation: A review and comparative study

Expert Systems With Applications, 2023
, Tingting Zhang
exaly  

An Effective Ensemble Method for Missing Data Imputation

International Journal of Information and Computer Security, 2022
Bikash Baruah, Manash Pratim Dutta, Dhruba K. Bhattacharyya   +2 more
openaire   +1 more source

Multiple imputation using nearest neighbor methods

Information Sciences, 2021
Gerhard Tutz, Shahla Faisal
exaly  

Imputation Method for Multidimensional Data

2023 3rd International Conference on Intelligent Technologies (CONIT), 2023
Jay Naik, Anil Jadhav
openaire   +1 more source

Improved Methods of Imputation for Clustering

2023 9th International Conference on Smart Computing and Communications (ICSCC), 2023
E V Veena, K P Pushpalatha
openaire   +1 more source