Journal article
A transfer-based additive LS-SVM classifier for handling missing data
IEEE Transactions on Cybernetics, Vol.50(2), pp.739-752
2020
Abstract
The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.
Details
- Title
- A transfer-based additive LS-SVM classifier for handling missing data
- Authors/Creators
- G. Wang (Author/Creator) - Australian Artificial Intelligence InstituteJ. Lu (Author/Creator) - Australian Artificial Intelligence InstituteK-S Choi (Author/Creator) - Hong Kong Polytechnic UniversityG. Zhang (Author/Creator) - Australian Artificial Intelligence Institute
- Publication Details
- IEEE Transactions on Cybernetics, Vol.50(2), pp.739-752
- Publisher
- IEEE
- Identifiers
- 991005543722307891
- Copyright
- © 2018 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.145 Classification Algorithms
- Web Of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Computer Science, Cybernetics
- ESI research areas
- Computer Science