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A Construction of Robust Representations for Small Data Sets Using Broad Learning System
- Source :
- IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51:6074-6084
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Feature processing is an important step for modeling and can improve the accuracy of machine learning models. Feature extraction methods can effectively extract features from high-dimensional data sets and enhance the accuracy of tasks. However, the performance of feature extraction methods is not stable in low-dimensional data sets. This article extends the broad learning system (BLS) to a framework for constructing robust representations in low-dimensional and small data sets. First, the BLS changed from a supervised prediction method to an ensemble feature extraction method. Second, feature extraction methods instead of random mapping are used to generate mapped features. Third, deep representations, called enhancement features, are learned from the ensemble mapped features. Fourth, data for generating mapped features and enhancement features can be randomly selected. The ensemble of mapped features and enhancement features can provide robust representations to enhance the performance of downstream tasks. A label-based autoencoder (LA) is embedded in the BLS framework as an example to show the effectiveness of the framework. A random LA (RLA) is presented to generate more different features. The experimental results show that the BLS framework can construct robust representations and significantly promote the performance of machine learning models.
- Subjects :
- Artificial neural network
business.industry
Computer science
Feature extraction
Pattern recognition
Construct (python library)
Autoencoder
Computer Science Applications
Data modeling
Human-Computer Interaction
Small data sets
Control and Systems Engineering
Feature (computer vision)
Task analysis
Artificial intelligence
Electrical and Electronic Engineering
business
Software
Subjects
Details
- ISSN :
- 21682232 and 21682216
- Volume :
- 51
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Accession number :
- edsair.doi...........f78e717ab6a74635abf3b76d7e262af3