Abstract
Correct classification of patterns from images is one of the challenging tasks and has become the focus of much research in areas of machine learning and computer vision in recent era. Images are described by many variables like shape, texture, color and spectral for practical model building. Hundreds or thousands of features are extracted from images, with each one containing only a small amount of information. The selection of optimal and relevant features is very important for correct classification and identification of benign and malignant tumors in breast cancer dataset. In this paper we analyzed different feature selection algorithms like best first search, chi-square test, gain ratio, information gain, recursive feature elimination and random forest for our dataset. We also proposed a ranking technique to all the selected features based on the score given by different feature selection algorithms.
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Verma, K., Singh, B.K., Tripathi, P., Thoke, A.S. (2015). Review of Feature Selection Algorithms for Breast Cancer Ultrasound Image. In: Barbucha, D., Nguyen, N., Batubara, J. (eds) New Trends in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-16211-9_3
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DOI: https://doi.org/10.1007/978-3-319-16211-9_3
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