Open Access Open Access  Restricted Access Subscription or Fee Access

A Fast PSO-ELM for Cancer Classification

V. Sivaraj, Dr. S. Sukumaran


Cancer is caused by uncontrolled and abnormal cells and it may spread through the blood stream or lymphatic system to further parts of the body. To explore the possibilities for classification of cancer, the researchers are started to perform using gene expression data. But still there are a lot of issues which is to be solved. So, this work introduced the fast PSO with ELM technique for cancer classification problems. This work implemented fast PSO method for multicategory classification of cancer cells. The PSO will provide the optimized output as the input to ELM. Evaluation is carried out for the proposed Fast PSO-ELM and the proposed approach achieves better classification accuracy.


Cancer Classification, Gene Expression, ELM, Fast PSO-ELM

Full Text:



Sun J, Chen W, Fang W, Wun X, Xu W. Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization. Engineering Applications of Artificial Intelligence. 2012 Mar 31;25(2):376-91.

Kumar PG, Victoire TA, Renukadevi P, Devaraj D. Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm. Expert Systems with Applications. 2012 Feb 1;39(2):1811-21.

Lam YK, Tsang PW, Leung CS. PSO-based K-Means clustering with enhanced cluster matching for gene expression data. Neural Computing and Applications. 2013 Jun 1;22(7-8):1349-55.

Sahu B, Mishra D. A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Engineering. 2012 Dec 31;38:27-31.

Mishra S, Shaw K, Mishra D. A new meta-heuristic bat inspired classification approach for microarray data. Procedia Technology. 2012 Dec 31;4:802-6.

Li X, Yin M. Multiobjective binary biogeography based optimization for feature selection using gene expression data. NanoBioscience, IEEE Transactions on. 2013 Dec;12(4):343-53.

Abdi MJ, Hosseini SM, Rezghi M. A novel weighted support vector machine based on particle swarm optimization for gene selection and tumor classification. Computational and mathematical methods in medicine. 2012 Jul 26;2012.

Mandal M, Mukhopadhyay A. A multiobjective PSO-based approach for identifying non-redundant gene markers from microarray gene expression data. InComputing, Communication and Applications (ICCCA), 2012 International Conference on 2012 Feb 22 (pp. 1-6). IEEE.

Noman N, Palafox L, Iba H. Reconstruction of gene regulatory networks from gene expression data using decoupled recurrent neural network model. InNatural Computing and Beyond 2013 Jan 1 (pp. 93-103). Springer Japan.

Kuo RJ, Syu YJ, Chen ZY, Tien FC. Integration of particle swarm optimization and genetic algorithm for dynamic clustering. Information Sciences. 2012 Jul 15;195:124-40.

Han F, Yao HF, Ling QH. An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing. 2013 Sep 20;116:87-93.

Han, Fei, Wei Sun, and Qing-Hua Ling. "A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information." (2014): e97530.

Karthikeyan T, Balakrishnan R. Microarray Gene Expression and Multiclass Cancer Classification Using Improved PSO Based Evolutionary Fuzzy ELM Classifier with ICGA Gene Selection. International Review on Computers and Software (IRECOS). 2013 Oct 31;8(10):2532-9.

Selvaraj G, Janakiraman S. Improved feature selection based on particle swarm optimization for liver disease diagnosis. InSwarm, Evolutionary, and Memetic Computing 2013 Jan 1 (pp. 214-225). Springer International Publishing.

Yang S, Han F, Guan J. A hybrid gene selection and classification approach for microarray data based on clustering and PSO. InEmerging Intelligent Computing Technology and Applications 2013 Jan 1 (pp. 88-93). Springer Berlin Heidelberg.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.