Feature Selection with Harmony Search for Classification: A Review

Norfadzlan, Yusup and Azlan, Mohd Zain and Nur Fatin Liyana, Mohd Rosely and Suhaila Mohamad, Yusuf (2021) Feature Selection with Harmony Search for Classification: A Review. In: 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology - ICEST, Medan, Indonesia.

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Abstract

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).

Item Type: Proceeding (Paper)
Uncontrolled Keywords: Data Mining, Feature Selection, Nature Inspired Metaheuristic Algorithm, Harmony Search, Classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Yusup
Date Deposited: 10 Dec 2021 00:35
Last Modified: 10 Dec 2021 00:35
URI: http://ir.unimas.my/id/eprint/37074

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