RF-EKHO: Random Forest with Enhanced Krill Herd Optimization Algorithm for proficient Detection of Outliers in Data with High-Dimensions
M Rao Batchanaboyina1, Nagaraju Devarakonda2
1M Rao Batchanaboyina, Research Scholar, Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, INDIA.
2Nagaraju Devarakonda, Infirmation Technolgy, Lakireddy Bali Reddy College of Engineering, Mylavaram, INDIA.

Manuscript received on 20 April 2019 | Revised Manuscript received on 26 May 2019 | Manuscript published on 30 May 2019 | PP: 744-748 | Volume-8 Issue-1, May 2019 | Retrieval Number: F2888037619/19©BEIESP
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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: The detection of outliers is a challenging issue in the case of data with high dimensions. It is extensively used in distinct fields of study like social networks, knowledge discovery and statistics. To maintain the network privacy and security in social networks identify structural abnormalities in a constructive way, which are different from the typical behavior of the social network. In this paper, we propose a hybrid model to discover outliers in social networks utilizing Random Forest (RF) and Enhanced Krill Herd Optimization (EKHO) algorithm. The RF is used to enhance the execution and exactness of general procedure and it is a productive classification strategy. The leaves per tree and the trees per the forest are the two parameters of RF. Experimental results shows the efficiency and success of proposed method in terms of accuracy, detection rate, and computational time.
Index Terms: Outlier Detection, Social Networks, Random Forest (RF), Enhanced Krill Herd Optimization (EKHO) Algorithm.

Scope of the Article: Social Networks