An Improved Feature Selection Algorithm for Harris Hawk optimization Based on Hybrid Strategy
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- An Improved Feature Selection Algorithm for Harris Hawk optimization Based on Hybrid Strategy
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An oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy
AbstractHybrid data lead to overfitting in machine learning models, which may reduce the accuracy of classification. Feature selection can not only reduce the computational cost of processing hybrid data but also improve the accuracy of classification. ...
Highlights- Mutual information entropy is presented to measure the uncertainty of a hybrid information system.
- Max-relevance and minimal redundancy model (MRMR-model) is put forward.
- A feature selection algorithm (denoted as MRMR) is designed.
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Association for Computing Machinery
New York, NY, United States
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