Rule Discovery Based Classification On Biological Dataset Using Ant Colony Optimization
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2015, Vol 4, Issue 8
Abstract
Classification systems have been widely utilized in medical domain to explore patient’s data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. Data mining can be done by using different functionalities. Classification is one of them. Classification is a data mining technique that assigns objects to a predefined classes or labels. The aim of classification is to classify the objects into target class. On the other hand biology inspired algorithms such as Genetic Algorithms(GA) and Swarm based approaches like Particle Swarm Optimization (PSO) and Ant Colonies Optimization (ACO) were used in solving many data mining problems. In this project, binary classification is considered as an area of problem. The main aim of this project is to discover the classification rule on biological dataset using ant miner by calculating accuracy function depends upon pheromone update levels. Ant miner uses rule induction algorithm that occupies collective intelligence to construct classification rules.
Authors and Affiliations
M V S S Krishna Bharat, Srinivasa Babji Josyula
Keywords
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