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  • 标题:Knowledge Based Analysis of Various Statistical Tools in Detecting Breast Cancer
  • 本地全文:下载
  • 作者:S. Aruna ; S.P. Rajagopalan ; L.V. Nandakishore
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2011
  • 卷号:1
  • 期号:2
  • 页码:37-45
  • DOI:10.5121/csit.2011.1205
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Na.ve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer.
  • 关键词:J48; Na.ve Bayes; RBF neural networks; Simple Cart; Support vector machines.
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