Exploration on Document Taxonomy by Ganb Algorithm

In this research, we propose an integrated classification GANB algorithm that combines a feature extractor with classifier to construct model. The automates the examination of raw pre-processed unstructured documents. Following extraction, categorization generates meaningful classes based on supplied features. study uses genetic (GA) for extraction and Naïve Bayes(NB) purposes. simulation evaluates suggested model's accuracy, sensitivity, specificity, f-measure using various performance indicators. Over Medline cancer datasets, gets higher rate than existing approaches.