Extracting a cancer model by enhanced ant colony optimisation algorithm. (1st January 2014)

• Record Type: Journal Article
• Authors: Shamsaee, Reza; Fathy, Mahmood; Masoudi-Nejad, Ali
• Is Part Of: International journal of data mining and bioinformatics. Volume 10:Number 1(2014)
• Page Start: 83
• Page End: 97
• Publication Date: 2014-01-01
• Subjects: mining -- abstract model -- ant colony optimisation and microarray -- cancer model; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Although Ant-Miner has been used with relative ease for datasets with categorical data and small-sized feature vectors, microarray datasets, which contain a few samples with large amount of genes, are a totally different story. The Ant-Miner is an ant colony optimisation algorithm that extracts predictive rules from datasets and intrinsically works on discrete values. This study has developed a new algorithm, "Enhanced Ant-Miner" (EAM), based on previous works. EAM deals with continuous attributes as well as categorical ones and presents its captured models in the form of predictive rules. EAM has been tested versus SVM, CN2, K-means and hierarchical clustering and the results show that EAM is the best in the context of predictive accuracy. Additionally, its agent-based nature gives it a much more charming ability to speed up the whole process when compared to other trivial miners.