We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Hybridization of PSO-ABC Based Ensemble Classification Model for High Dimensional Medical Datasets.
- Authors
Kumari, G. Lalitha; Rao, N. Naga Malleswara
- Abstract
As the size and dimensionality of microarray datasets increase, it is vital to select essential features for data classification. Traditionally, ranking and selection measures are used to select the essential features from the high dimensional feature space. However, these measures are used to improve the data classification rate with limited number of instances and features space. Feature selection is one of the challenging issues for microarray datasets due to noise, sparsity and missing values. Traditional feature selection models such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used to select the highly weighted features for data classification. But these models require high computational memory and time for data classification. In this paper, a hybrid PSO+ABC based feature selection model is designed and implemented on microarray disease datasets. Proposed hybrid feature selection model is applied on multiple classification models to improve the true positive rate and error rate for different dimensional datasets. Experimental results are simulated on different microarray disease dataset and these results proved that the hybrid feature selection model has high true positive rate and minimal mean squared error rate compared to the traditional models.
- Subjects
AMERICAN Broadcasting Co.; PLANT hybridization; EXAMPLE
- Publication
International Journal of Simulation -- Systems, Science & Technology, 2018, Vol 19, Issue 6, p1
- ISSN
1473-8031
- Publication type
Article
- DOI
10.5013/IJSSST.a.19.06.02