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An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data

Zainal Abidin, Nadzurah and Ismail, Amelia Ritahani (2021) An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data. International Journal of Advances in Intelligent Informatics, 7 (3). pp. 304-317. ISSN 2442-6571 E-ISSN 2548-3161

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Abstract

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. However, despite the favorable points, KNN still imposes undesirable circumstances. KNN suffers from high time complexity, choosing the right k, and different functions. Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. Our GOA is designed to find the best value of k and optimize the imputed value from KNN that maximizes the imputation accuracy. Experimental evaluation for different types of datasets collected from UCI, with various rates of missing values ranging from 10%, 30%, and 50%. Our proposed algorithm has achieved promising results from the experiment conducted, which outperformed other methods, especially in terms of accuracy.

Item Type: Article (Journal)
Uncontrolled Keywords: Grasshopper, KNN, Imputation accuracy, GOA, Missing data
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Amelia Ritahani Ismail
Date Deposited: 11 Apr 2022 08:05
Last Modified: 11 Apr 2022 08:05
URI: http://irep.iium.edu.my/id/eprint/97580

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