Hybrid ACO-PSO-GA-DE Algorithm for Big Data Classification
Anju Bala1, Priti2
1Anju Bala, Research Scholar, Department of Computer Science and Applications, M.D.U, Rohtak, India.
2Priti, Assistant Professor, Department of Computer Science and Applications, M.D.U, Rohtak, India.
Manuscript received on 08 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 703-708 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1708078219/19©BEIESP | DOI: 10.35940/ijrte.B1708.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper designs a technique to classify big data efficiently. This work considers the processing of big data as an optimization problem due to the trade-off between accuracy and time and solves this optimization problem by using a meta-heuristic approach. The HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm is designed by using the support vector machine (SVM) along with hybrid ACO-PSO-GA-DE algorithm that hybrids exploration capability of ACO with exploitation capability of PSO whose balance is maintained using modified GA. The GA has been modified by using the DE algorithm. The presented technique performs classification efficiently as shown in results on seven datasets using different analysis parameters due to balanced exploration and exploitation search with fast convergence.
Index Terms: Accuracy, ACO, Big Data, Classification, DE, GA, PSO.
Scope of the Article: Classification