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Sample selection-based hierarchical extreme learning machine
- Source :
- Neurocomputing. 377:95-102
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Large amounts of training data in machine learning can keep the accuracy high to a certain extent, but the time costs are high because of the exorbitant amount of data and their dimensionality. Therefore, how to simultaneously select the most useful training data set and extract the main features of the samples, especially for image data, are essential problems that urgently need to be solved in the field of large-scale machine learning. Herein, a training sample selection method that is based on the fuzzy c-means clustering algorithm (FCM) is proposed for the problems. It first utilises condensed nearest neighbour (CNN) to make a preliminary selection of training samples. Then, it utilises the FCM to get the centres of the selected data, and, finally, it effectively condenses the sample using a compression parameter. Meanwhile, considering the critical influence of the sample features on the classification model, this paper selects the hierarchical extreme learning machine (H-ELM) model to better solve the classification task. Based on this, the paper presents the FCM-CNN-H-ELM framework for data classification, which combines FCM-Based CNN and H-ELM. The results of the experiments show that the proposed training sample selection method and classification framework can guarantee consistent, even higher, prediction results with a small number of training samples, and significantly reduce the training time.
- Subjects :
- 0209 industrial biotechnology
Training set
Computer science
business.industry
Cognitive Neuroscience
Data classification
Sample (statistics)
02 engineering and technology
Machine learning
computer.software_genre
Fuzzy logic
Field (computer science)
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Cluster analysis
computer
Selection (genetic algorithm)
Curse of dimensionality
Extreme learning machine
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 377
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........7502b77c498729057606ac77c3b4cb76