Classification of Erythematous - Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient
| Autor: | Şengül Bayrak, Ahmet Sertbas, Yeliz Karaca |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Dimensionality reduction Feature extraction Pattern recognition 02 engineering and technology Linear discriminant analysis Support vector machine Data set 030207 dermatology & venereal diseases 03 medical and health sciences Kernel (linear algebra) 0302 clinical medicine Wavelet Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Mathematics |
| Zdroj: | Computational Science and Its Applications – ICCSA 2018 ISBN: 9783319951645 ICCSA (2) |
| DOI: | 10.1007/978-3-319-95165-2_8 |
| Popis: | Feature extraction is a kind of dimensionality reduction which refers to the differentiating features of a dataset. In this study, we have worked on ESD_Data Set (33 attributes), composed of clinical and histopathological attributes of erythematous-squamous skin diseases (ESDs) (psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, pityriasis rubra pilaris). It’s aimed to obtain distinguishing significant attributes in ESD_Data Set for a successful classification of ESDs. We have focused on three areas: (a) By applying 1-D continuous wavelet coefficient analysis, Principle Component Analysis and Linear Discriminant Analysis to ESD_Data Set; w_ESD Data Set, p_ESD Data Set and l_ESD Data Set were formed. (b) By applying Support Vector Machine kernel algorithms (Linear, Quadratic, Cubic, Gaussian) to these datasets, accuracy rates were obtained. (c) w_ESD Data Set had the highest accuracy. This study seeks to identify deficiencies in literature to determine the distinguishing significant attributes in ESD_Data Set to classify ESDs. |
| Databáze: | OpenAIRE |
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