Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 548483, 7 pages
http://dx.doi.org/10.1155/2014/548483
Research Article

PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

1Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China
2School of Business Administration, South China University of Technology, Guangzhou, Guangdong 510640, China

Received 16 April 2014; Accepted 4 May 2014; Published 25 May 2014

Academic Editor: Xin-She Yang

Copyright © 2014 Xiaoyong Liu and Hui Fu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Marcin Wozniak, Dawid Polap, Robert K. Nowicki, Christian Napoli, Giuseppe Pappalardo, and Emiliano Tramontana, “Novel approach toward medical signals classifier,” 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7, . View at Publisher · View at Google Scholar
  • Divya Tomar, and Sonali Agarwal, “Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes,” Advances in Artificial Neural Systems, vol. 2015, pp. 1–10, 2015. View at Publisher · View at Google Scholar
  • Peng Chen, Lifen Yuan, Yigang He, and Shuai Luo, “An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis,” Neurocomputing, 2016. View at Publisher · View at Google Scholar
  • Mohammad Shehab, Ahamad Tajudin Khader, and Mohammed Azmi Al-Betar, “A survey on applications and variants of the cuckoo search algorithm,” Applied Soft Computing, 2017. View at Publisher · View at Google Scholar
  • M. Prabukumar, L. Agilandeeswari, and K. Ganesan, “An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier,” Journal of Ambient Intelligence and Humanized Computing, 2017. View at Publisher · View at Google Scholar
  • C. Selvi, and E. Sivasankar, “A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach,” Soft Computing, 2017. View at Publisher · View at Google Scholar
  • Shinq-Jen Wu, Van-Hung Pham, and Thi-Nga Nguyen, “Two-phase optimization for support vectors and parameter selection of support vector machines: Two-class classification,” Applied Soft Computing Journal, vol. 59, pp. 129–142, 2017. View at Publisher · View at Google Scholar
  • Baraa M. Abed, Khalid Shaker, Hothefa Shaker, Ahmad F. Alwan, Hamid A. Jalab, Ali Mohammed Mansoor, and Ihsan Salman Al-Gburi, “A hybrid classification algorithm approach for breast cancer diagnosis,” IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference, pp. 269–274, 2017. View at Publisher · View at Google Scholar