Publication Type:

Conference Paper

Source:

Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Institute of Electrical and Electronics Engineers Inc., p.374-380 (2019)

ISBN:

9781538692769

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062778561&doi=10.1109%2fSSCI.2018.8628897&partnerID=40&md5=ce0bba9a47015d4d7f2e3163ff8a60ed

Keywords:

Artificial intelligence, Classification accuracy, compactness, Computation time, Data reduction, decision making, Feature extraction, Filter techniques, Learning systems, Real time systems, Wrapper techniques

Abstract:

The growth of technologies has led to connection of various devices to the internet. The devices communicate through internet to query the state or information associated with them. These systems require to do real time decision making from the data acquired. In such systems it is important for the learning model to provide faster processing of the data. Large dimension of features makes it a difficult task. Feature reduction is a decisive aspect used in machine learning for dimensionality reduction of data and performance improvement of models. This paper provides a comparative study on the performance of the various feature reduction techniques. © 2018 IEEE.

Notes:

cited By 0; Conference of 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 ; Conference Date: 18 November 2018 Through 21 November 2018; Conference Code:144687

Cite this Research Publication

P. Vijai and P. Sivakumar, B., “Performance comparison of feature reduction techniques in-terms of compactness, computation time and accuracy”, in Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 374-380.