Journal of Data Processing G. Mohana Prabha, S. Chitra 8 2 2018 10.6025/jdp/2018/8/2/74-81 http://www.dline.info/jdp/fulltext/v8n2/jdpv8n2_3.pdf A novel Ant Colony Optimization algorithm (ACO) is 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.