Integrating clustering with evolutionary feature selection using ENORA and SToWVector

The rapid growth of textual data from sources such as social media, blogs, and digital libraries has intensified the demand for scalable and semantically informed classification methods. This study introduces a hybrid framework that integrates unsupervised clustering, evolutionary feature selection, and semantic interpretation to enhance automatic text classification. The approach combines the SToWVector representation with a Multi-Objective Evolutionary Search (MOES) strategy optimized through the ENORA algorithm, while employing the NaiveBayesMultinomial classifier for evaluation. Semantic interpretation is incorporated via ontological reasoning, enabling the model to capture latent conceptual relationships among terms and thereby complement both the clustering and feature selection processes. Experimental evaluations on benchmark and large-scale datasets (SMS Spam and Euronews) demonstrate the robustness of the framework, including a scenario in which 100% accuracy was achieved. The proposed method outperforms traditional models and achieves competitive results against deep learning-based classifiers. These findings underscore the framework’s adaptability and effectiveness in managing high-dimensional unstructured text, while preserving interpretability through symbolic reasoning. • A hybrid model combining clustering, MOES-ENORA, and symbolic reasoning is proposed. • SToWVector is enhanced with evolutionary multi-objective feature selection. • NaiveBayesMultinomial outperforms deep models like BERT with smaller model size. • Ontological reasoning adds semantic interpretability to automatic text classification. • Experiments confirm high accuracy ( > 99%) and reduced complexity across datasets.