Transformation Based Tri-Level Feature Selection Approach Using Wavelets and Swarm Computing for Prostate Cancer Classification

Sunil Kumar Prabhakar, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Prostate Cancer is a cancer that occurs in the prostate- a small walnut shaped gland in men. This gland helps in the production of seminal fluid which is used to nourish and transport the sperm. One of the most common types of cancer in men is prostate cancer. A microarray dataset contains the microarray gene expression information. On a genome wide scale, gene expression profiles make it easy to analyze the patterns between genes and cancers, however the analysis of gene expression data is very difficult as it has a high dimensionality and low Signal to Noise Ratio (SNR). In this paper, a transformation-based Tri-level feature selection using wavelets for prostate cancer classification has been proposed. For the input microarray data, initially wavelets are applied and then the essential features are selected. Then the standardized gene selection techniques are implemented such as Relief-F, Fishers Score, Information Gain and SNR for a second level feature selection stage. Finally, before proceeding to classification, a third level feature selection by means of optimization techniques are implemented. The optimization techniques incorporated in this work are Marriage in Honey Bee Optimization Algorithm (MHBOA), Migrating Birds Optimization Algorithm (MBOA), Salp Swarm Optimization Algorithm (SSOA) and Whale Optimization Algorithm (WOA). This kind of an approach is totally new, and the best results show when SNR with WOA is classified with Artificial Neural Network (ANN) giving a classification accuracy of 99.48%. The second highest classification accuracy of 99.22% is obtained when Relief-F test with MBOA is classified with Naïve Bayesian Classifier (NBC).

Original languageEnglish
Article number9130690
Pages (from-to)127462-127476
Number of pages15
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT), Department of Artificial Intelligence, Korea University, under Grant 2019-0-00079.

Publisher Copyright:
© 2013 IEEE.


  • Prostate cancer
  • classification
  • feature selection
  • optimization

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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