Artikel ; Online: Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.
PeerJ. Computer science
2021 Band 7, Seite(n) e344
Abstract: Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine ... ...
Abstract | Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance. |
---|---|
Sprache | Englisch |
Erscheinungsdatum | 2021-01-25 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ISSN | 2376-5992 |
ISSN (online) | 2376-5992 |
DOI | 10.7717/peerj-cs.344 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
Zusatzmaterialien
Kategorien
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.
Fernleihe an ZB MED
Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.