Function approximation based on fuzzy rules extracted from partitioned numerical data

Function approximation based on fuzzy rules extracted from partitioned numerical data

We present an efficient method for extracting fuzzy rules directly from numerical input-output data for function approximation problems. First, we convert a given function approximation problem into a pattern classification problem. This is done by dividing the universe of discourse of the output variable into multiple intervals, each regarded as a class, and then by assigning a class to each o...

Fuzzy rules extraction directly from numerical data for function approximation

An Efficient Algorithm for Inducing Fuzzy Rules from Numerical Data

This paper proposes a modified but more powerful algorithm for inducing fuzzy if-then rules from numerical data. Data mining is performed before the process of fuzzy inference in view of the presence of noise in data. Therefore, the minimum number of learning attributes can be used to induce fuzzy rules automatically from training examples. The results obtained by this modified method demonstra...

Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification

Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then" rules that are easy to understand, verify, and extend. This paper presents methods for extracting fuzzy rules for both function approximation and pattern classification. The rule extraction methods are based on estimating clusters in the data; each cluster obtained corresponds to a fuzzy rule that relat...

On Mining Fuzzy Classification Rules for Imbalanced Data

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...