Abstract
In the article a method for reducing the input data coming to the input of a neural network of the Multilayer Perceptron type is proposed. The data that was used for the analysis are related to one of the most malignant tumors - malignant melanoma. They refer to patients with malignant melanoma that were registered in Oncology Complex Center in Burgas town. The data and contain information about age, sex, marital status of the patient, date of diagnosis, name of the disease according to the International statistical classification of diseases and health problems. The InterCriteria Analysis (ICA) approach was used to analyze the relationships between these parameters.
A new improved structure and algorithm for increasing the learning speed of the Multilayer Perceptron (MLP) neural network are proposed. The algorithm is hybrid and includes determining the degrees of consonance and dissonance between the observed parameters. From the pairs of parameters (from ICA) with the highest consonance and with the lowest dissonance, one parameter is removed. For the purposes of MLP training, one of the values of the fuzzy pair of parameters is redundant because it does not carry additional information, and its removal reduces the number of neurons in the input layer of the neural network. In turn, this reduces the total error generated by each neuron. Another positive part is the faster learning of the neural network due to the lightweight architecture.
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Acknowledgment
The authors are grateful for the support provided by the Bulgarian National Science Fund under Grant Ref. No. KP-06-N22/1/2018 “Theoretical research and applications of InterCriteria Analysis”. The authors declare that there is no conflict of interest regarding the publication of this paper.
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Sotirov, S., Petrova, Y., Bozov, H., Sotirova, E. (2022). A Hybrid Algorithm for Multilayer Perceptron Design with Intuitionistic Fuzzy Logic Using Malignant Melanoma Disease Data. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-09173-5_77
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