Method for Classification of Cancers with Partial Least Squares Regression as Feature Selector with Kernel SVM

Nimrita Koul, Sunilkumar Manvi, Bryan Gardiner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

Classification of cancers according to the exact place of origin in the body is an important research problem that is being addressed both clinically and computationally. Application of data science and machine learning to a vast volume of imaging and genomics data regarding cancers has enabled computational researchers to accurately classify the tumor samples according to their place of origin. In current work, we developed a method to classify tumors using partial least squares regression as feature selector and support vector
machine classifiers. We have evaluated our approach on three cancer gene expression datasets and found classification accuracies of 100% in some circumstances. The comparison is conducted with standard classification methods like Decision Trees and simple Support Vector Machines with respect to standard performance parameters and the time taken for classification. The comparison in terms of training and testing accuracies and the time taken for classification results show that our method performs consistently better than conventional
methods.
Original languageEnglish
Title of host publication2022 International Conference for Advancement in Technology (ICONAT)
PublisherIEEE
ISBN (Electronic)978-1-6654-2577-3
ISBN (Print)978-1-6654-2578-0
DOIs
Publication statusPublished (in print/issue) - 10 Mar 2022
EventIEEE International Conference for Advancement in Technology - , India
Duration: 20 Jan 202221 Jan 2022

Publication series

Name2022 International Conference for Advancement in Technology (ICONAT)
PublisherIEEE

Conference

ConferenceIEEE International Conference for Advancement in Technology
Country/TerritoryIndia
Period20/01/2221/01/22

Keywords

  • Classification
  • Machine Learning
  • Support Vector Machines
  • Regression Analysis

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