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论文类型：期刊论文

发表时间：2015-09-01

发表刊物：INTERNATIONAL JOURNAL OF FUZZY SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：17

期号：3

页面范围：471-483

ISSN号：1562-2479

关键字：Mammographic risk analysis; Kernel-based Fuzzy-rough sets; Nearest-neighbour algorithms; Classification

摘要：Mammographic risk analysis is an important task for assessing the likelihood of a woman developing breast cancer. It has attracted much attention in recent years as it can be used as an early risk indicator when screening patients. In this paper, a kernel-based fuzzy-rough nearest-neighbour approach to classification is employed to address the issue of the assessment of mammographic risk. Four different breast tissue density assessment metrics are employed to support this study, and the performance of the proposed approach is compared with alternative nearest-neighbour-based classifiers and other popular learning classification techniques. Systematic experimental results show that the work employed here generally improves the classification performance over the others, measured using criteria such as classification accuracy rate, root mean squared error and the kappa statistics. This demonstrates the potential of kernel-based fuzzy-rough nearest-neighbour classification as a robust and reliable tool for mammographic risk analysis.