Feature Selection with Harmony Search for Classification: A Review
In the area of data mining, feature selection is an important task for classification and dimensionality reduction. Feature selection is the process of choosing the most relevant features in a datasets. If the datasets contains irrelevant features, it will not only affect the training of the classif...
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| Main Authors: | , , , |
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| Format: | Proceeding |
| Language: | en |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/37074/1/Norfadzlan%20Yusup.pdf http://ir.unimas.my/id/eprint/37074/ https://www.scitepress.org/Papers/2018/100420/100420.pdf |
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| Summary: | In the area of data mining, feature selection is an important task for classification and dimensionality reduction. Feature selection is the process of choosing the most relevant features in a datasets. If the datasets contains irrelevant features, it will not only affect the training of the classification process but also the accuracy of the model. A good classification accuracy can be achieved when the model correctly predicted the class labels. This paper gives a general review of feature selection with Harmony Search (HS) algorithm for classification in various application. From the review, feature selection with HS algorithm shows a good performance as compared to other metaheuristics algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). |
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