A crucial method for reducing the dimensionality issue in DM (Data Mining) tasks is FS (Feature Selection). Conventional techniques for FS do not scale well in vast spaces. The HPSO-IKM approach has a rather long processing time, so future studies will keep enhancing the technique's stages to reduce the duration of detection. PSO's poor local search capability and lagging convergence in the refining search phase prevent it from mitigating the effects of poor initialization by reducing the greatest number of IC (Intra-Clustering) faults. This paper suggests a novel approach to the dimensionality issue, in which a good feature subset is produced through combining the correlation metric using clustering. Following Z Score Normalization (ZSN) for pre-processing, a computational model is constructed to identify the pertinent features based on pertinent constraints, and a structure is developed by extracting features via Principal Component Analysis (PCA). Next, utilizing Multi-Objective Glowworm Swarm Optimization using Improved Fuzzy C-Means Clustering (MOGWO-IFCM), unnecessary features are removed, and non-redundant features are chosen from every cluster based on correlation measures. This approach employs the IFCM technique for optimizing the initial clustering center after receiving the optimal solution as an initial clustering center with the GSO (Glowworm Swarm Optimization) technique. Utilizing the Modified Long Short-Term Memory (MLSTM) classifier, the suggested approach is tested on UCI datasets, and the outcomes are contrasted with those of other well-known FS methods. Percent-wise criteria are employed to confirm the accuracy of the suggested technique with varying numbers of pertinent features. The suggested technique's accuracy and efficiency are demonstrated by the outcomes of the experiment.
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