NDLI: An Analysis of Properties of Malignant Cases for Imbalanced Breast Thermogram Feature Classification

Content Provider	IEEE Xplore Digital Library
Author	Krawczyk, B. Schaefer, G.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK (Schaefer, G.) \|\| Dept. of Syst. & Comput. Networks, Wroclaw Univ. of Technol., Wrocław, Poland (Krawczyk, B.)
Abstract	Medical thermography has been demonstrated an effective and inexpensive method for detecting breast cancer, in particular for tumors in early stages and in dense tissue. Image features can be extracted from breast thermograms and used in a pattern classification stage for automated diagnosis and hence as a second objective opinion or for screening purposes. One of the main challenges for applying machine learning algorithms to this task is the high imbalance ratio between class distributions in the available training data. In this paper, we carefully examine the properties of the malignant minority class in order to gain insight into the nature of the data. We identify different types of minority class samples present in a breast thermogram dataset comprising about 150 cases. Using the gained knowledge, we analyse the performance of three state-of-the-art ensemble classifiers, a cost-sensitive one, one based on over-sampling and one using under-sampling, to evaluate which objects are the most difficult to classify correctly. Experimental analysis shows that there is a strong correlation between the type of minority sample and the performance of specific classifier ensemble types.
Starting Page	305
Ending Page	309
File Size	370226
Page Count	5
File Format	PDF
ISBN	9781479921904
DOI	10.1109/ACPR.2013.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-05
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Medical data analysis Imbalanced classification Pattern classification Feature extraction Breast cancer Pattern recognition Multiple classifier system Ensemble classifier
Content Type	Text
Resource Type	Article

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