An algorithm proposed for semi-supervised learning in cancer detection

Semi-supervised learning, a relatively new area in machine learning, represents a blend of supervised and unsupervised learning, and has the potential of reducing the need of expensive labelled data whenever only a small set of labelled examples is available. In this paper an algorithm for Semi Supe...

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Bibliographic Details
Published in:	International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011) pp. 860 - 864
Main Authors:	Aruna, S, Rajagopalan, S.P, Nandakishore, L.V
Format:	Conference Proceeding
Language:	English
Published:	Stevenage IET 2011 The Institution of Engineering & Technology
Subjects:	Biology and medical computing Data handling techniques Knowledge engineering techniques Other topics in statistics medical diagnostic computing training examples cancer detection SVM-naive Bayes classifiers data analysis semisupervised learning WBC dataset labelled examples UCI machine learning depository unsupervised learning labelled data Gist-SVM cancer Bayes methods learning (artificial intelligence) SVM classifier
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Summary:	Semi-supervised learning, a relatively new area in machine learning, represents a blend of supervised and unsupervised learning, and has the potential of reducing the need of expensive labelled data whenever only a small set of labelled examples is available. In this paper an algorithm for Semi Supervised learning for detecting Cancer is proposed. We use the few labelled data to train the SVM classifier with Gist-SVM. We enlarge the number of training examples with SVM-Naive Bayes classifiers. We used WBC dataset from UCI Machine learning depository for our proposed methodology.
ISBN:	9789380430003 9380430000
DOI:	10.1049/cp.2011.0487