@article{MAKHILLJEAS201813315514,
    title = {EMOPS: An Enhanced Multi-Objective Particle Swarm Based
Classifier for Poorly Understood Cancer Patterns},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {3},
    pages = {580-587},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.580.587},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.580.587},
    author = {N.P.,S. and},
    keywords = {Cancer pattern classifications,microarray,multi-objective Particle Swarm,SVM,gene expression,classification},
    abstract = {Microarray based cancer pattern classification is one of the popular techniques in bioinformatics
research. At the same time, it was noticed that for studying the expression levels through the gene expression
profiling experiments, thousands of genes have to be simultaneously studied to understand the patterns of the
gene expression or cancer pattern. This research proposed an efficient cancer pattern classifier called an
Enhanced Multi-Objective Particle Swarm (EMOPS) and it is studied thoroughly in terms of memory utilization,
execution time (processing time), sensitivity, specificity, classification accuracy and F-score. The results were
compared with that of the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized
Fuzzy Rough Set based semi supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle
Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this research
considered a few cancer patterns namely bladder, breast, colon, endometrial, kidney, leukemia, lung, melanoma,
mom-hodgkin, pancreatic, prostate and thyroid. From our experimental results, it was noticed that the
proposed model outperforms the identified three classifiers in terms of memory utilization, execution time
(processing time), sensitivity, specificity, classification accuracy and F-score.}
    }