Support-vector-based fuzzy neural network for pattern classification

Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

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A fuzzy neural network for pattern classification and feature selection

Fuzzy Sets and Systems ◽

10.1016/s0165-0114(02)00050-7 ◽

2002 ◽

Vol 130 (1) ◽

pp. 101-108 ◽

Cited By ~ 45

Author(s):

Rui-Ping Li ◽

Masao Mukaidono ◽

I.Burhan Turksen

Keyword(s):

Neural Network ◽

Feature Selection ◽

Pattern Classification ◽

Fuzzy Neural Network ◽

Fuzzy Neural

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HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX

Technological and Economic Development of Economy ◽

10.3846/tede.2018.6394 ◽

2018 ◽

Vol 24 (6) ◽

pp. 2161-2178 ◽

Cited By ~ 19

Author(s):

Fernando García ◽

Francisco Guijarro ◽

Javier Oliver ◽

Rima Tamošiūnienė

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Stock Index ◽

Machine Learning Techniques ◽

Support Vector ◽

Accurate Information ◽

Investment Strategies ◽

Trend Prediction ◽

Fuzzy Neural ◽

Input Variables

Intraday trading rules require accurate information about the future short term market evolution. For that reason, next-day market trend prediction has attracted the attention of both academics and practitioners. This interest has increased in recent years, as different methodologies have been applied to this end. Usually, machine learning techniques are used such as artificial neural networks, support vector machines and decision trees. The input variables of most of the studies are traditional technical indicators which are used by professional traders to implement investment strategies. We analyse if these indicators have predictive power on the German DAX-30 stock index by applying a hybrid fuzzy neural network to predict the one-day ahead direction of index. We implement different models depending on whether all the indicators and oscillators are used as inputs, or if a linear combination of them obtained through a factor analysis is used instead. In order to guarantee for the robustness of the results, we train and apply the HyFIS models on randomly selected subsamples 10,000 times. The results show that the reduction of the dimension through the factorial analysis generates more profitable and less risky strategies.

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