MVIBPM: DESIGN OF A MISSING VALUE IDENTIFICATION TECHNIQUE VIA BIOINSPIRED PREDICTIVE MODELING
DOI:
https://doi.org/10.48047/xp84hw78Keywords:
Missing, Value, NB, kNN, SVM, DF, EHO, Accuracy, Precision, Recall, OptimizationsAbstract
Detecting absent values in time-series data samples is a challenging signal-processing task that
requires pattern analysis, proactive modeling, and regression methods. Researchers propose various models
to optimize the efficiency of missing value identification techniques. Most of them remain intricate and
unsuitable for extensive information sets. Additionally, the limited effectiveness of basic models when
dealing with extensive datasets restricts their applicability for real-time uses. In order to address these
challenges, this article introduces a new Elephant Herding Optimization (EHO) Model that aims to enhance
an effective ensemble classifier for identifying missing values, particularly suited for feature-based data
samples.
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