Exploration on Document Taxonomy by Ganb Algorithm
R. Sathish Babu
Dr. R. Sathish Babu*, Assistant Professor, Department of Computer and Information Science, Annamalai University, Annamalai Nagar, India.
Manuscript received on 27 March 2022. | Revised Manuscript received on 30 March 2022. | Manuscript published on 30 April 2022. | PP: 100-103 | Volume-11 Issue-4, April 2022. | Retrieval Number: 100.1/ijeat.D34850411422 | DOI: 10.35940/ijeat.D3485.0411422
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this research, we propose an integrated classification GANB algorithm that combines a feature extractor with a classifier to construct a classification model. The feature extractor automates the examination of raw pre-processed unstructured documents. Following feature extraction, categorization generates meaningful classes based on the supplied features. The study uses a genetic algorithm (GA) for feature extraction and Naïve Bayes(NB) for classification purposes. The simulation evaluates the suggested classification model’s accuracy, sensitivity, specificity, and f-measure using various performance indicators. Over the Medline cancer datasets, the suggested GANB gets a higher classification rate than existing approaches.
Keywords: Genetic Algorithm, Naïve Bayes, Feature Extraction, Classification.
Scope of the Article: Classification.