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A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set
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Author(s): Linkon Chowdhury (East Delta University, Bangladesh), Md Sarwar Kamal (University of Technology Sydney, Australia), Shamim H. Ripon (East West University, Bangladesh), Sazia Parvin (University of New South Wales, Australia), Omar Khadeer Hussain (University of New South Wales, Australia), Amira Ashour (Tanta University, Egypt)and Bristy Roy Chowdhury (BGC Trust University, Bangladesh)
Copyright: 2024
Pages: 20
Source title:
Research Anthology on Bioinformatics, Genomics, and Computational Biology
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/979-8-3693-3026-5.ch001
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Abstract
Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.
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