You are in:Home/Publications/ Linguistic Hedges Fuzzy Feature Selection For Erythemato-Squamous Diseases. In: Soft Computing Applications, Advances in Intelligent Systems and Computing (AISC), Springer-Verlag Berlin Heidelberg 2013; 195, pp. 487–500. DOI: 10.1007/978-3-642-33941-7_43.

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Linguistic Hedges Fuzzy Feature Selection For Erythemato-Squamous Diseases. In: Soft Computing Applications, Advances in Intelligent Systems and Computing (AISC), Springer-Verlag Berlin Heidelberg 2013; 195, pp. 487–500. DOI: 10.1007/978-3-642-33941-7_43.
Authors: Azar AT, El-Said SA, Balas VE, Olariu T
Year: 2013
Keywords: Not Available
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Volume: Not Available
Issue: Not Available
Pages: Not Available
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Local/International: International
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Abstract:

The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is pre- sented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition mod- els: 50–50%, 60–40%, 70–30% and 80–20%. The highest classification accura- cy of 95.7746% was achieved for 80–20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50–50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70–30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60–40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80–20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.

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