Background:
Mining knowledge from microarray data is one of the popular research
topics in biomedical informatics. Gene selection is a significant research trend in biomedical data
mining, since the accuracy of tumor identification heavily relies on the genes biologically relevant
to the identified problems.
Objective:
In order to select a small subset of informative genes from numerous genes for tumor
identification, various computational intelligence methods were presented. However, due to the
high data dimensions, small sample size, and the inherent noise available, many computational
methods confront challenges in selecting small gene subset.
Methods:
In our study, we propose a novel algorithm PSONRS_KNN for gene selection based on
the particle swarm optimization (PSO) algorithm along with the neighborhood rough set (NRS) reduction
model and the K-nearest neighborhood (KNN) classifier.
Results:
First, the top-ranked candidate genes are obtained by the GainRatioAttributeEval preselection
algorithm in WEKA. Then, the minimum possible meaningful set of genes is selected by
combining PSO with NRS and KNN classifier.
Conclusion:
Experimental results on five microarray gene expression datasets demonstrate that the
performance of the proposed method is better than existing state-of-the-art methods in terms of
classification accuracy and the number of selected genes.
A New Image Segmentation Algorithm Based on Particle Swarm Optimization and Rough Set