Improving Recall Values in Breast Cancer Diagnosis with Incremental Background Knowledge



Cancer diagnosis is generally the process of using some form of physical or genetic tests or exams, usually referred as patient data, to detect the disease. One of the main problems with cancer diagnosis systems is the lack of labeled data, as well as the difficulties of labeling pre-existing unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in cancer diagnosis. The possible availability of this kind of data for some applications makes it an appealing source of information. In this work we explore an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to better aid decisions, namely by improving recall values. The defined incremental SVM margin-based method was tested in the Wisconsin-Madison breast cancer diagnosis problem to examine the effectiveness of such techniques in supporting diagnosis.




IEEE World Congress on Computational Intelligence (WCCI 2010), July 2010


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Year 2013 : 1 citations

 Somdatta Patra and Mr. GourSundarMitra Thakur. A proposed neuro-fuzzy model for adult asthma disease diagnosis. Computer Science & Information Technology (CS & IT), 3(2):191–205, 2013.