Welcome to the InfoSci Platform
Reference Hub16
Modified Firefly Algorithm With Chaos Theory for Feature Selection: A Predictive Model for Medical Data

Modified Firefly Algorithm With Chaos Theory for Feature Selection: A Predictive Model for Medical Data

Sujata Dash, Ruppa Thulasiram, Parimala Thulasiraman
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 20
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781522566373|DOI: 10.4018/IJSIR.2019040101
Cite Article Cite Article

MLA

Dash, Sujata, et al. "Modified Firefly Algorithm With Chaos Theory for Feature Selection: A Predictive Model for Medical Data." IJSIR vol.10, no.2 2019: pp.1-20. http://doi.org/10.4018/IJSIR.2019040101

APA

Dash, S., Thulasiram, R., & Thulasiraman, P. (2019). Modified Firefly Algorithm With Chaos Theory for Feature Selection: A Predictive Model for Medical Data. International Journal of Swarm Intelligence Research (IJSIR), 10(2), 1-20. http://doi.org/10.4018/IJSIR.2019040101

Chicago

Dash, Sujata, Ruppa Thulasiram, and Parimala Thulasiraman. "Modified Firefly Algorithm With Chaos Theory for Feature Selection: A Predictive Model for Medical Data," International Journal of Swarm Intelligence Research (IJSIR) 10, no.2: 1-20. http://doi.org/10.4018/IJSIR.2019040101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.