Abstract
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.
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Karaca, Y., Sertbaş, A., Bayrak, Ş. (2018). Classification of Erythematous - Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_8
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