Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 5, Pages 1581-1586Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2868836
Keywords
Extreme learning machine (ELM); feature selection; neural networks; random projection
Categories
Funding
- National Key Research and Development Program of China [2018YFB1107403]
- National Natural Science Foundation of China [61773316]
- Natural Science Foundation of Shaanxi Province [2018KJXX-024]
- Fundamental Research Funds for the Central Universities [3102017AX010]
- Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences
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Random projection is a popular machine learning algorithm, which can be implemented by neural networks and trained in a very efficient manner. However, the number of features should be large enough when applied to a rather large-scale data set, which results in slow speed in testing procedure and more storage space under some circumstances. Furthermore, some of the features are redundant and even noisy since they are randomly generated, so the performance may be affected by these features. To remedy these problems, an effective feature selection method is introduced to select useful features hierarchically. Specifically, a novel criterion is proposed to select useful neurons for neural networks, which establishes a new way for network architecture design. The testing time and accuracy of the proposed method are improved compared with traditional methods and some variations on both classification and regression tasks. Extensive experiments confirm the effectiveness of the proposed method.
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