Title: An efficient missing value imputation and evaluation using GK-KH means and HTR-RNN

Authors: C.V.S.R. Syavasya; A. Lakshmi Muddana

Addresses: Department of Computer Science and Engineering, Gitam Deemed University, Rudraram, Hyderabad 502329, India ' Department of Computer Science and Engineering, Gitam Deemed University, Rudraram, Hyderabad 502329, India

Abstract: The accuracy of the data mining (DM) outcomes might be affected by mining and analysing incomplete datasets with missing values (MV). Thus, a complete dataset is created by the imputation of MV, which makes the analysis easier. An effectual missing values imputation (MVI) is proposed and evaluated utilising Gaussian kernel-K harmonic means (GK-KH means) and hyperbolic tangent radial-recurrent neural networks (HTR-RNN) to combat this issue. At first, preprocessing is performed on the input data as of the CKD dataset wherein the duplicate form of the data gets eradicated. Next, the missing data are handled by ignoring them; and utilising GK-KH means, the MV is imputed. Next, the data are rationalised into a structured format. Then, SDRM-DHO selects the most optimal features as of the extracted features. Lastly, the HTR-RNN classifier accepts these chosen features as input. Proposed work performed well in more accurate missing value imputation.

Keywords: missing value imputation; K harmonic means; Gaussian kernel function; recurrent neural network; swap displacement reversion operation.

DOI: 10.1504/IJBIDM.2024.135130

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.1, pp.25 - 46

Received: 27 Dec 2021
Accepted: 08 Feb 2022
Published online: 01 Dec 2023 *