|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Author(s):||Gheyas, Iffat A|
|Title:||A neural network-based framework for the reconstruction of incomplete data sets|
|Citation:||Gheyas IA & Smith L (2010) A neural network-based framework for the reconstruction of incomplete data sets. 10th Brazilian Symposium on Neural Networks (SBRN2008), Salvador, Brazil, 26.10.2008-30.10.2008, Neurocomputing, 73 (16-18), pp. 3039-3065. http://www.sciencedirect.com/science/article/pii/S0925231210003188; https://doi.org/10.1016/j.neucom.2010.06.021.|
|Conference Name:||10th Brazilian Symposium on Neural Networks (SBRN2008)|
|Conference Dates:||2008-10-26 - 2008-10-30|
|Conference Location:||Salvador, Brazil|
|Abstract:||The treatment of incomplete data is an important step in the pre-processing of data. We propose a novel nonparametric algorithm Generalized regression neural network Ensemble for Multiple Imputation (GEMI). We also developed a single imputation (SI) version of this approach-GESI. We compare our algorithms with 25 popular missing data imputation algorithms on 98 real-world and synthetic datasets for various percentage of missing values. The effectiveness of the algorithms is evaluated in terms of (i) the accuracy of output classification: three classifiers (a generalized regression neural network, a multilayer perceptron and a logistic regression technique) are separately trained and tested on the dataset imputed with each imputation algorithm, (ii) interval analysis with missing observations and (iii) point estimation accuracy of the missing value imputation. GEMI outperformed GESI and all the conventional imputation algorithms in terms of all three criteria considered.|
|Status:||VoR - Version of Record|
|Rights:||The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.|
|Gheyas_Smith_Neurocomputing_19102010.pdf||Fulltext - Published Version||1.33 MB||Adobe PDF||Under Embargo until 2997-12-31 Request a copy|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.