Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection

• Autorzy: Noel Pérez , Miguel Angel Guevara , Augusto Silva , Isabel Ramos , Joana Loureiro
• Języki publikacji: EN

Abstrakty

EN

This paper proposed a comprehensive algorithm for building machine learning classifiers for Breast Cancer diagnosis based on the suitable combination of feature selection methods that provide high performance over the Area Under receiver operating characteristic Curve (AUC). The new developed method allows both for exploring and ranking search spaces of image-based features, and selecting subsets of optimal features for feeding Machine Learning Classifiers (MLCs). The method was evaluated using six mammography-based datasets (containing calcifications and masses lesions) with different configurations extracted from two public Breast Cancer databases. According to the Wilcoxon Statistical Test, the proposed method demonstrated to provide competitive Breast Cancer classification schemes reducing the number of employed features for each experimental dataset.(original abstract)

Słowa kluczowe

PL

Algorytmy; Choroby; Medycyna; Uczenie maszynowe

EN

Algorithms; Illness; Medicine; Machine learning

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2014
• Tom: 2
• Strony: 209-217

Twórcy

autor

Noel Pérez

• Campus da FEUP

autor

Miguel Angel Guevara

• Universitário de Santiago, Portugal

autor

Augusto Silva

• Universitário de Santiago, Portugal

autor

Isabel Ramos

• Centro Hospitalar São João

autor

Joana Loureiro

• Centro Hospitalar São João

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