Open AccessExploration on Document Taxonomy by Ganb Algorithm
Author(s) -
R. Babu
Publication year - 2022
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d3485.0411422
Subject(s) - computer science , extractor , classifier (uml) , naive bayes classifier , artificial intelligence , feature extraction , pattern recognition (psychology) , data mining , categorization , statistical classification , feature (linguistics) , machine learning , support vector machine , engineering , linguistics , philosophy , process engineering
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.