Open AccessAn improved K-Nearest neighbour with grasshopper optimization algorithm for imputation of missing data
Author(s) -
Nadzurah Zainal Abidin,
Amelia Ritahani Ismail
Publication year - 2021
Publication title -
ijain (international journal of advances in intelligent informatics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.183
H-Index - 9
eISSN - 2548-3161
pISSN - 2442-6571
DOI - 10.26555/ijain.v7i3.696
Subject(s) - imputation (statistics) , missing data , computer science , k nearest neighbors algorithm , data mining , nearest neighbour , pattern recognition (psychology) , artificial intelligence , algorithm , machine learning
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.