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A Study on Wrapper-Based Feature Selection Algorithm for Leukemia Dataset

  • M. J. Abinash
  • V. Vasudevan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

In many fields, big data play a predominant role such as in research, business, biological science, and many other fields in our day-to-day activities. It is mainly the voluminous amount of structured, semi-structured, and unstructured data. It is the base from the data mining. So the knowledge discovery using this data is very difficult. Bioinformatics is an interdisciplinary of biology and information technology; the gene expression or the microarray data are analyzed using some softwares. These gene data are grown higher and higher, so the analyze and the classification are more difficult among these growing big data. So we focus on analyzing these data for cancer classification. The proposed work discusses the SVM-based wrapper feature selection for cancer classification. The cancer dataset are applied in two feature selection algorithms, and among them, the wrapper-based SVM method is made best for feature selection for cancer classification.

Keywords

Gene Feature selection Support vector machines (SVM) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyKalasalingam UniversityVirudhunagarIndia

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