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.