Abstract
DNA Microarray data is a high-dimensional data that enables the researchers to analyze the expression of many genes in a single reaction quickly and in an efficient manner. Its characteristics such as small sample size, class imbalance, and data complexity causes it difficult to classified. Feature selection is a process that automatically selects features that are most relevant to the predictive modeling in dataset. This research aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony (ABC) approach. The result is compared with other evolution algorithms, which is Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result is that feature selection using ABC has a better result at classification using k-Nearest Neighbor (k-NN) and Decision Tree (DT), but has a slightly higher fracture of features compared to GA and PSO algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
