Abstract
Presence of missing values (MV) in gene expression data is commonplace. It significantly affects the performance of statistical analysis and machine learning algorithms. Discarding objects or attributes with missing values and inappropriate estimation of MVs lead to high information loss and misleading results. So, it is necessary to have an accurate technique for missing value imputation. In this paper, we present a novel correlation based missing value imputation technique for gene expression datasets. We refer to our method as NCBI. We compare the estimation accuracy of our technique with two widely used methods such as KNNI and KMI, on four benchmark datasets by randomly knocking out data values as missing. Our technique can estimate missing values almost 20–25% more accurately than KNNI and KMI in all datasets.
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Chowdhury, H.A., Ahmed, H.A., Bhattacharyya, D.K., Kalita, J.K. (2020). NCBI: A Novel Correlation Based Imputing Technique Using Biclustering. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_43
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DOI: https://doi.org/10.1007/978-981-13-9042-5_43
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