Melanoma important features selection using random forest approach

Paja, Wiesław; Wrzesień, Mariusz

doi:10.1109/hsi.2013.6577857

Cited by 9 publications

(1 citation statement)

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“…They evaluated the performance of the classification methods by using the kappa, accuracy, and MCC values. Paja and Wrzesie [7] explained the results of a feature selection method which was used to find the most important or all important features that characterize melanocytic spots on the skin. Maulik et al [8] show the effectiveness of the proposed transductive SVM scheme in the framework of transductive inference learning, using two feature selection methods.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm

Lakshmi

Kumar²,

Suresh³

2014

Advances in Intelligent Systems and Computing

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In this paper, we propose a new intelligent prediction system to predict more accurately the presence of heart diseases effectively from feature-selected medical dataset. For this purpose, a new weighted genetic algorithm is proposed for selecting very important features from the dataset for improving the prediction accuracy of the disease. In this proposed intelligent prediction system, the data are preprocessed using the new weighted genetic algorithm and the new weighted fuzzy C-means clustering algorithm is used for effective fragmentation. Finally, we have used the ID3 algorithm for classification which is useful for making effective decision.

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Section: Related Workmentioning

confidence: 99%