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Dynamic feature selection algorithm based on Q-learning mechanism
- Source :
- Applied Intelligence. 51:7233-7244
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Feature selection is a technique to improve the classification accuracy of classifiers and a convenient data visualization method. As an incremental, task oriented, and model-free learning algorithm, Q-learning is suitable for feature selection, this study proposes a dynamic feature selection algorithm, which combines feature selection and Q-learning into a framework. First, the Q-learning is used to construct the discriminant functions for each class of the data. Next, the feature ranking is achieved according to the all discrimination functions vectors for each class of the data comprehensively, and the feature ranking is doing during the process of updating discriminant function vectors. Finally, experiments are designed to compare the performance of the proposed algorithm with four feature selection algorithms, the experimental results on the benchmark data set verify the effectiveness of the proposed algorithm, the classification performance of the proposed algorithm is better than the other feature selection algorithms, meanwhile the proposed algorithm also has good performance in removing the redundant features, and the experiments of the effect of learning rates on the our algorithm demonstrate that the selection of parameters in our algorithm is very simple.
- Subjects :
- Computer science
business.industry
Q-learning
Feature selection
Pattern recognition
02 engineering and technology
Set (abstract data type)
Data visualization
Discriminant
Discriminant function analysis
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Selection algorithm
Selection (genetic algorithm)
Subjects
Details
- ISSN :
- 15737497 and 0924669X
- Volume :
- 51
- Database :
- OpenAIRE
- Journal :
- Applied Intelligence
- Accession number :
- edsair.doi...........788c2f9e22baeb871b8fe2338fc9be2e