Using Cascade Generalization and Neural Networks to Select Cryotherapy method for Warts

In this paper, complementary neural networks are applied to cascade generalization. Complementary neural networks comprise two neural networks trained to predict truth and falsity values. Two levels of cascade generalization are implemented in this paper. Two approaches are proposed. First, a neural network is trained in the base level whereas complementary neural networks are trained in meta level of cascade generalization. Second, complementary neural networks are trained in both levels of cascade generalization. The proposed methods are used to select cryotherapy method for wart treatment. The cryotherapy data set is obtained from UCI machine learning repository. Ten-fold cross validation is used in the experiment. The proposed approach gives 98.89% accuracy which higher than the existing methods which are cascade generalization and stacked generalization.

[1]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[2]  João Gama,et al.  Cascade Generalization , 2000, Machine Learning.

[3]  Pawalai Kraipeerapun,et al.  Using stacked generalization and complementary neural networks to predict Parkinson's disease , 2015, 2015 11th International Conference on Natural Computation (ICNC).

[4]  Pawalai Kraipeerapun,et al.  Using Falsity Data in the Stacking Technique , 2016, 2016 International Conference on Computational Intelligence and Applications (ICCIA).

[5]  Saeid Nahavandi,et al.  An expert system for selecting wart treatment method , 2017, Comput. Biol. Medicine.

[6]  Kehua Guo,et al.  Deep Convolution Neural Network Discriminator for Distinguishing Seborrheic Keratosis and Flat Warts , 2017, 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[7]  R. Alizadehsani,et al.  Intralesional immunotherapy compared to cryotherapy in the treatment of warts , 2017, International journal of dermatology.

[8]  Selahaddin Batuhan Akben,et al.  Predicting the success of wart treatment methods using decision tree based fuzzy informative images , 2018 .

[9]  Puneet Mathur,et al.  Feature Selection for Cryotherapy and Immunotherapy Treatment Methods Based on Gravitational Search Algorithm , 2018, 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT).

[10]  F. Kayaalp,et al.  A Hybrid Classification Example in the Diagnosis of Skin Disease with Cryotherapy and Immunotherapy Treatment , 2018, 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).

[11]  Rukiye Uzun,et al.  Use of Support Vector Machines to Predict the Success of Wart Treatment Methods , 2018, 2018 Innovations in Intelligent Systems and Applications Conference (ASYU).

[12]  Sabita Khatri,et al.  Enhancing Decision Tree Classification Accuracy through Genetically Programmed Attributes for Wart Treatment Method Identification , 2018 .

[13]  Hardi TALABANI,et al.  Impact of Various Kernels on Support Vector Machine Classification Performance for Treating Wart Disease , 2018, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).

[14]  Vanessa Souza Araujo,et al.  Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy , 2019, Big Data Cogn. Comput..