Improving Feature Selection Using Elite Breeding QPSO on Gene Data set for Cancer Classification

  • Poonam Chaudhari
  • Himanshu Agarwal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


This paper focuses on feature set selection on microarray gene expression for cancer classification. The gene expression data are incommensurate when it comes to number of genes and number of samples. This imbalance makes it important to study feature selection algorithms from the complex gene expression data. We conducted a research with quantum particle swarm optimization with elitist breeding (EBQPSO) on gene data sets. To the best of our knowledge, the exploration of the elitist is not taken into account for deep searching and classification applications with genetic data sets. Our contribution in this paper is to use EBQPSO algorithm on gene data sets for classification of cancer. The algorithm is tested with supervised and unsupervised learning approach, viz. support vector machine, J48 and neural network. The results show that EBQPSO outperforms particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) algorithms in terms of precision and recall value.


EBQPSO Elitist breeding Precision Recall 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Symbiosis Institute of TechnologyPuneIndia

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