Title: Ensemble features selection method as tool for breast cancer classification

Authors: Noel Pérez; Augusto Silva; Isabel Ramos

Addresses: Institute of Mechanical Engineering and Industrial Management (INEGI), Campus da FEUP, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal ' Institute of Electronics and Telematics Engineering of Aveiro (IEETA), Campus Universitário de Santiago, 3810-193 Aveiro, Portugal ' Faculty of Medicine – Centro Hospitalar São João (FMUP-HSJ), Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal

Abstract: This work aims to gather experimental evidence of features relevance, as well as finding a breast cancer classification scheme that provides the high performance over the area under receiver operating characteristic curve (AUC). An ensemble feature selection method (named RMean) based on the mean criteria for indexing relevant features is presented. The proposed method provided better classification performances (statistically significant) than those who constitute the baseline, attaining AUC scores of 0.7775 with the support vector machine on microcalcifications dataset and 0.9440 with the feed-forward-backpropagation neural network classifier on masses dataset. The most relevant features for microcalcifications classification were: mammographic stroma distortion, density, right bottom quadrant, perimeter, standard deviation, entropy, and angular second moment. Meanwhile, to classify masses were: mammographic stroma distortion, mammographic calcification, mammographic nodules, density, circularity, roughness, and shape.

Keywords: mammography based features; ensemble feature selection; feature relevance analysis; machine learning classifiers; breast cancer classification; support vector machines; SVM; microcalcification; neural networks.

DOI: 10.1504/IJIM.2015.073019

International Journal of Image Mining, 2015 Vol.1 No.2/3, pp.224 - 244

Received: 03 Feb 2015
Accepted: 03 Feb 2015

Published online: 12 Nov 2015 *

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