IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Nature Inspired Feature Selector for Effective Data Classification in Big Data Frameworks

Nature Inspired Feature Selector for Effective Data Classification in Big Data Frameworks
View Sample PDF
Author(s): Appavu Alias Balamurugan Subramanian (K.L.N. College of Information Technology, India)
Copyright: 2019
Pages: 18
Source title: Nature-Inspired Algorithms for Big Data Frameworks
Source Author(s)/Editor(s): Hema Banati (Dyal Singh College, India), Shikha Mehta (Jaypee Institute of Information Technology, India)and Parmeet Kaur (Jaypee Institute of Information Technology, India)
DOI: 10.4018/978-1-5225-5852-1.ch004

Purchase

View Nature Inspired Feature Selector for Effective Data Classification in Big Data Frameworks on the publisher's website for pricing and purchasing information.

Abstract

In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise. And also, time complexity is the major issues in existing approach. In order to rectify these issues our proposed method made use of efficient feature subset selection in high dimensional data. We are considering the input dataset is the high dimensional micro array dataset. Initially, we have to select the optimal features so that our proposed technique employed Social Spider Optimization (SSO) algorithm. Here the traditional Social Spider Optimization is modified with the help of fruit fly optimization algorithm. Next the selected features are the input for the classifier. The classification is performed using optimized radial basis function based neural network (ORBFNN) technique to classify the micro array data as normal or abnormal data. The effectiveness of RBFNN is optimized by means of artificial bee colony algorithm (ABC).

Related Content

Bhargav Naidu Matcha, Sivakumar Sivanesan, K. C. Ng, Se Yong Eh Noum, Aman Sharma. © 2023. 60 pages.
Lavanya Sendhilvel, Kush Diwakar Desai, Simran Adake, Rachit Bisaria, Hemang Ghanshyambhai Vekariya. © 2023. 15 pages.
Jayanthi Ganapathy, Purushothaman R., Ramya M., Joselyn Diana C.. © 2023. 14 pages.
Prince Rajak, Anjali Sagar Jangde, Govind P. Gupta. © 2023. 14 pages.
Mustafa Eren Akpınar. © 2023. 9 pages.
Sreekantha Desai Karanam, Krithin M., R. V. Kulkarni. © 2023. 34 pages.
Omprakash Nayak, Tejaswini Pallapothala, Govind P. Gupta. © 2023. 19 pages.
Body Bottom