2 research outputs found
Improving the Interpretability of Support Vector Machines-based Fuzzy Rules
Support vector machines (SVMs) and fuzzy rule systems are functionally
equivalent under some conditions. Therefore, the learning algorithms developed
in the field of support vector machines can be used to adapt the parameters of
fuzzy systems. Extracting fuzzy models from support vector machines has the
inherent advantage that the model does not need to determine the number of
rules in advance. However, after the support vector machine learning, the
complexity is usually high, and interpretability is also impaired. This paper
not only proposes a complete framework for extracting interpretable SVM-based
fuzzy modeling, but also provides optimization issues of the models.
Simulations examples are given to embody the idea of this paper.Comment: 8 pages, 2 figure
A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM
This paper proposed a model to predict the stock price based on combining
Self-Organizing Map (SOM) and fuzzy-Support Vector Machines (f-SVM). Extraction
of fuzzy rules from raw data based on the combining of statistical machine
learning models is base of this proposed approach. In the proposed model, SOM
is used as a clustering algorithm to partition the whole input space into the
several disjoint regions. For each partition, a set of fuzzy rules is extracted
based on a f-SVM combining model. Then fuzzy rules sets are used to predict the
test data using fuzzy inference algorithms. The performance of the proposed
approach is compared with other models using four data setsComment: 6 pages, 3 figure