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  • 标题:A LOGISTIC REGRESSION BASED HYBRID MODEL FOR BREAST CANCER CLASSIFICATION
  • 本地全文:下载
  • 作者:Tina Elizabeth Mathew ; K S Anil Kumar
  • 期刊名称:Indian Journal of Computer Science and Engineering
  • 印刷版ISSN:2231-3850
  • 电子版ISSN:0976-5166
  • 出版年度:2020
  • 卷号:11
  • 期号:6
  • 页码:899-906
  • DOI:10.21817/indjcse/2020/v11i6/201106201
  • 出版社:Engg Journals Publications
  • 摘要:Data mining techniques are being used for breast cancer classification and good performance accuracy has been obtained while using the techniques individually or as ensembles. A notable problem is the skewed nature of the data which leads to imbalance among the output classes. The minority class being the negative class usually are smaller in number than the positive majority class. This usually leads to a moderate accuracy value for the classifier. The correct classification of minority cases is a significant problem. All classes need to be given equal importance during classification. In this study a hybrid model based on Logistic Regression is implemented with class balancing and ant search techniques and the performance is evaluated on the two class Wisconsin breast cancer dataset. A performance accuracy of 99.4% was obtained.
  • 关键词:Synthetic Minority Over-sampling Technique (SMOTE);Oversampling (OS);Random Undersampling (US);Neural Networks (NN);Random Forest (RF);Logistic Regression (LR);Support Vector Machines (SVM);Naïve Bayes (NB);Ant Search (AS)
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