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Blum, A. and Chawla, S. (2001) Learning from Labeled and Unlabeled Data Using Graph Mincuts. Intl. Conference on Machine Learning.

has been cited by the following article:

• TITLE: Semi Advised SVM with Adaptive Differential Evolution Based Feature Selection for Skin Cancer Diagnosis

  AUTHORS: Ammara Masood, Adel Al-Jumaily

  KEYWORDS: Classification, Feature Selection, Skin Cancer, Support Vector Machine, Differential Evolution

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.11, November 19, 2015

  ABSTRACT: Automated diagnosis of skin cancer is an important area of research that had different automated learning methods proposed so far. However, models based on insufficient labeled training data can badly influence the diagnosis results if there is no advising and semi supervising capability in the model to add unlabeled data in the training set to get sufficient information. This paper proposes a semi-advised support vector machine based classification algorithm that can be trained using labeled data together with abundant unlabeled data. Adaptive differential evolution based algorithm is used for feature selection. For experimental analysis two type of skin cancer datasets are used, one is based on digital dermoscopic images and other is based on histopathological images. The proposed model provided quite convincing results on both the datasets, when compared with respective state-of-the art methods used for feature selection and classification phase.