• E. Balraj M.Kumarasamy College of Engineering,Karur,India
  • R. Sujatha M.Kumarasamy College of Engineering,Karur,Indiaii




ACO – Ant Colony Optimization, DAG-directed Acyclic graph, MuLAM (Multi-Label Ant-Miner), hAnt-Miner (Hierarchical Classification Ant-Miner),


A novel Ant Colony Optimization algorithm (ACO) combined for the hierarchical multi- label classification problem of protein function prediction. This kind of problem is mainly focused on biometric area, given the large increase in the number of uncharacterized proteins available for analysis and the importance of determining their functions in order to improve the current biological knowledge. Because it is known that a protein can perform more than one function and many protein functional-definition schemes are organized in a hierarchical structure, the classification problem in this case is an instance of a hierarchical multi-label problem. In this classification method, each class might have multiple class labels and class labels are represented in a hierarchical structure—either a tree or a directed acyclic graph (DAG) structure. A more difficult problem than conventional flat classification in this approach, given that the classification algorithm has to take into account hierarchical relationships between class labels and be able to predict multiple class labels for the same example. The proposed ACO algorithm discovers an ordered list of hierarchical multi-label classification rules.


Download data is not yet available.


1. Abdul Rauf Baig (2013) Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers. IN: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 17, NO. 5, OCTOBER 2013.
2. Otero F, Freitas A, Johnson C (2009) A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms. In: Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio 2009), LNCS 5483, Springer, pp 68–79
3. Consortium TGO (2008) Gene ontology: tool for the unification of biology. Nature Genetics 25:25– 29. Otero F, Freitas A, Johnson C (2009) Handling contin- uous attributes in ant colony classification algorithms.In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM-2009), IEEE, pp 225–231
4. Alves R, Delgado M, Freitas A (2008) Multi-label hierarchical classification of protein functions with artificial immune systems. In: Advances in Bioinformatics and Computational Biology (Proc. BSB-2008).
5. Bi R, Zhou Y, Lu F, Wang W (2007) Predicting Gene Ontology functions based on support vector machines and statistical significance estimation Neuro Computing 70.
6. Blockeel H, Schietgat L, Struyf J, Dzˇeroski S, Clare A (2006) Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics. In:PKDD-2006, LNAI 4213, pp 18–29
7. Barutcuoglu Z, Schapire R, Troyanskaya O (2006) Hierarchical multi-label prediction of gene function. Bioinformatics 22(7)

8. Chan A, Freitas A (2006) A new ant colony algorithm for multi-label classification with applications in bioinformatics. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp 27–34
9. Clare A, Karwath A, Ougham H, King R (2006) Functional bioinformatics for Arabidopsis thailana. Bioinformatics 22(9):1130–1136
10. Rousu J,Saunders C, Szedmak S, ShaWe-Tylor J (2006) Kernel Based learning of Hierarchical Multilevel classification Models. Journal of Machine learning Research pp1601-1626
11. Ruepp A, Zollner A, Maier D, Albermann K, Hani J, Mokrejs M, Tetko I, Guldener U, Mannhaupt G, Munsterkotter M, MeWes H (2004) The FunCat, a func- tional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acid Research 32(18):5539–5545
12. Cesa-Bianchi N, Zaniboni L, Collins M (2004) Incremental algorithms for hierarchical classification. Journal of Machine Learning Research pp 31–54
13. Blockeel H, Bruynooghe M, Dzˇeroski S, Ramon J, Struyf J (2002) Hierarchical multi classification. In: Dzˇeroski S, Raedt LD, Wrobel S (eds) Proceedings of the First SIGKDD Workshop on Multi-Relational Data Mining (MRDM 2002), University of Alberta, Edmon- ton, Canada, pp 21– 35
14. Parpinelli R, Lopes H, Freitas A (2002) Data mining with an ant colony optimization algorithm. IEEE
Transactions on Evolutionary Computation 6(4):321–332.
15. Mohanaprabha G, Balraj E, (2014) A hm Ant-miner using evolutionary algorithm.International Journal
of Innovative Research in Science, Engineering and Technology 1687-1692.




How to Cite

Balraj, E., & Sujatha, R. (2016). A SURVEY ON ANT COLONY OPTIMIZATION ALGORITHM. JOURNAL OF ADVANCES IN CHEMISTRY, 12(17), 5031–5038. https://doi.org/10.24297/jac.v12i17.989