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- W135360676 abstract "In this work, ANN and SVM, two most popular supervised machine learning techniques, are considered as the representatives and k-means clustering is used as representative of unsupervised learning. By analyzing the diagnosis result using Wisconsin Cancer Dataset (WBCD) which is commonly used among researchers who use machine learning methods for breast cancer diagnosis, it can be concluded that SVM outperforms in case of breast cancer diagnosis. The result is verified using two other breast cancer datasets. One is Cancer Dataset from UCI Machine Learning Repository and another one is Breast cancer dataset with Electrical Impedance Measurements in samples of freshly excised tissue. The purpose of the comparison is to choose the best solution in terms of performance. Another notable significance of the work is that accuracy of the recognition drops down severely if proper feature set is not used. One significant disadvantage of neural network" @default.
- W135360676 created "2016-06-24" @default.
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- W135360676 date "2013-12-26" @default.
- W135360676 modified "2023-09-23" @default.
- W135360676 title "An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis" @default.
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- W135360676 hasPublicationYear "2013" @default.
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