A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Ain:2022:ICDMW,

• author = "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and Mengjie Zhang",
• booktitle = "2022 IEEE International Conference on Data Mining Workshops (ICDMW)",
• title = "A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification",
• year = "2022",
• pages = "378--387",
• abstract = "Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes them expensive in real-time situations. Such methods achieve only moderate classification accuracy due to the limited model capacity and computational resources. In addition, most existing studies focus on improving classification accuracy using only raw images or the entire set of original attributes and remain unable to identify hidden patterns or causal information necessary to discriminate breast density classes. It is challenging to find high-quality knowledge when some attributes defining the data space are redundant or irrelevant. In this study, we present a novel attribute construction method using genetic programming (GP) for the task of breast density classification. To extract informative features from the raw mammographic images, wavelet decomposition, local binary patterns, and histogram of oriented gradients are used to include texture, local and global image properties. The study evaluates the goodness of the proposed method on two benchmark real-world mammographic image datasets and compares the results of the proposed GP method with eight conventional classification methods. The experimental results reveal that the proposed method significantly outperforms most of the commonly used classification methods in binary and multi-class classification tasks. Furthermore, the study shows the potential of G P for mammographic breast density classification by interpreting evolved attributes that highlight important breast density characteristics.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICDMW58026.2022.00057",
• ISSN = "2375-9259",
• month = nov,
• notes = "Also known as \cite{10031110}",

}

Genetic Programming entries for Qurrat Ul Ain Bing Xue Harith Al-Sahaf Mengjie Zhang

Citations