EEFR-R: extracting effective fuzzy rules for regression problems, through the cooperation of association rule mining concepts and evolutionary algorithms


Fuzzy rule-based systems, due to their simplicity and comprehensibility, are widely used to solve regression problems. Fuzzy rules can be generated by learning from data examples. However, this strategy may result in high numbers of rules that most of them are redundant and/or weak, and they affect the systems’ interpretability. Hence, in this paper, a new rule learning method, EEFR-R, is proposed to extract the effective fuzzy rules from regression data samples. This method is formed through the cooperation of association rule mining concepts and evolutionary algorithms in the three stages. Indeed, the components of a Mamdani fuzzy rule-based system are generated during the first two stages, and then, they will be refined through some modifications in the last stage. In EEFR-R, fuzzy rules are extracted from numerical data using the idea of Wang and Mendel’s method and utilizing the concepts of Support and Confidence; furthermore, a new rule pruning method is presented to refine these rules. By employing this method, non-effective rules can be pruned in three different modes as the preferences of a decision maker. The proposed model and its stages were validated using 19 real-world regression datasets. The experimental results and the conducted statistical tests confirmed the effectiveness of EEFR-R in terms of complexity and accuracy and in comparison with the three state-of-the-art regression solutions.

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    These advantages are general and may be variant depending on the application.

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    In mathematics, the number of elements of a set is called the cardinality of that set.


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Appendix A: overview of Mamdani FRBSs

The standard architecture of a fuzzy rule-based system consists of four main modules as below (Riza et al. 2015):

Fuzzification—The fuzzification module converts the crisp input values into linguistic ones. Linguistic values refer to ordinary concepts like high, medium, low, etc., which are used in our conversation to declare measures. MFs are defined based on these linguistic variables to map input values into fuzzy concepts.

Knowledge Base (KB)—KB is one of the essential components of FRBS. It comprises two fundamental parts including Data Base (DB) and Rule Base (RB). The DB includes fuzzy set definitions and the MF’s parameters while the RB contains a set of linguistic fuzzy If-Then rules. Fuzzy rules are applied in the decision-making process. They are written in the following format: Ifx is A, Theny is B where the If part is called antecedent and the Then part is called consequent of the rule. This rule is described as: If the antecedent conditions are satisfied, Then the consequent can be inferred.

Inference Engine—The inference engine is where FRBS performs the process of reasoning; it uses fuzzy If-Then rules and input data to make the inference operation.

Defuzzification module—In defuzzification procedure, the acquired output is transformed from a fuzzy value into a crisp one.

Among the different fuzzy inference systems (Timothy 2010), two types of Mamdani FIS (Mamdani 1977) and Takagi–Sugeno–Kang (TSK) FIS (Takagi and Sugeno 1985) have been widely used in the regression applications. The most important differences between these FISs are related to the consequent part of the rules, and accordingly, their aggregation and defuzzification methods are different. In Mamdani fuzzy inference systems, both antecedent and consequent parts of a rule are determined by fuzzy sets, while in TSK systems only the antecedent part is represented by fuzzy sets and the consequent part is computed through a weighted linear function or a constant value. Mamdani fuzzy systems are usually applied to get more interpretable models, whereas TSK systems focus on the accuracy and precision (Blej and Azizi 2016).

The general form of a fuzzy If-Then rule in Mamdani system is like this:

$$\begin{aligned} \mathrm{Rule}^i :&\mathbf {If} ~ X^1 ~ is ~ A_i^1 ~ and ~ X^2 ~ is ~ A_i^2 ~ and ~ \ldots ~ and ~ X^n ~ is ~ A_i^n \nonumber \\&\mathbf {Then} ~ Y ~ is ~ B_i ; ~ i=1, ~\ldots ,~ p \end{aligned}$$

where \(X^k\) is the kth input variable, n is the number of input variables, Y is the output variable, p is the number of all rules, and \(A_i^j\) and \(B_i\) are the linguistic variables defined by MFs in the DB. The main advantage of this composition of inputs and output is its close relationship with the way of human thinking.

Fig. 8

MFs of different variables; left figures are related to the initial MFs, and right figures are the same left MFs which have been tuned

After generating or learning the constituents of DB and RB, the process of inferring in Mamdani system is done through the following five steps:

  1. (1)

    Fuzzification of the input values using the defined MFs.

  2. (2)

    Applying the fuzzy operator (AND or OR) to the antecedents in order to combine the fuzzified inputs and obtain the rule strength \((h_i)\). \(h_i\) is also called firing strength of the ith rule. It measures the degree of matching the input vector \((X^1, X^2, \ldots , X^n)\) to the ith rule.

    $$\begin{aligned} h_i=T(A_i^1 (X^1), A_i^2 (X^2), \ldots , A_i^n (X^n)) \end{aligned}$$

    where \(A_i^k\) is the fuzzy value of the kth input variable \((X^k)\) and T is the T-norm conjunctive operator. Mamdani specifically recommended the use of the minimum T-norm (Mamdani 1977).

  3. (3)

    Indicating the final consequent for each rule; it is achieved by combining the computed rule strength and the linguistic fuzzy term related to the output of a certain rule as:

    $$\begin{aligned} B^{\prime }_i (Y)=T(h_i ,B_i (Y)) \end{aligned}$$

    \(B^{\prime }_i\) is the conclusive consequent of the ith rule, \(B_i\) is the fuzzy value of output variable Y and T is the T-norm conjunctive operator. Mamdani has also recommended the use of the minimum T-norm in this case (Mamdani 1977).

  4. (4)

    Aggregation of all rule’s consequent; this is usually done by using the fuzzy OR operator as:

    $$\begin{aligned} O(Y)=\bigcup _{i=1}^p B^{\prime }_i (Y) \end{aligned}$$

    where O(Y) is the result MF.

  5. (5)

    Defuzzification of the result; the output MF is not enough in many applications and the crisp value is needed. Some methods like centroid, weighted average, mean-max membership, etc., have been suggested computing the crisp value of the result. The most common method is centroid which uses the center of gravity of the fuzzy sets in which the crisp output y is calculated as:

    $$\begin{aligned} y= \int O(Y) Y \mathrm{d}y\diagup \int O(Y) \mathrm{d}y \end{aligned}$$

    Due to the capability of Mamdani FIS in generating simple and interpretable models and regarding our objective in reducing the complexity of regression solutions, it is used in this study. Mamdani weakness in getting lower accuracy (in comparison with TSK models), however, has been overcome using optimization methods.

Appendix B: an illustrative example of EEFR-R

In this section, an illustrative example of EEFR-R is described. For this purpose, the first problem of Table 2, Ele-1, has been taken into account. Ele-1 is a real-world benchmark problem which estimates the length of low-voltage lines in rural towns using some available inputs. This dataset includes 3 attributes, namely Inhabitants, Distance, and Length; Inhabitants and Distance are the system inputs which are used to estimate the output Length (Cordón et al. 1999).

Table 14 Results of rule generation

EEFR-R is performed for Ele-1 in the three stages as the descriptions of Sect. 3. In the following, the results of one run of EEFR-R for this dataset are demonstrated. In the preprocessing stage, Sect. 3.1, a set of CPs is generated for each variable as:

$$\begin{aligned}&S_\mathrm{Inhabitants}= & {} \left\{ 1,~20,~320 \right\}&\\&S_\mathrm{Distance}= & {} \left\{ 60,~362.5,~ 500.8,~665.83,~1673.3 \right\}&\\&S_\mathrm{Length}= & {} \left\{ 80,~1568,~3492.5,~7675\right\}&\end{aligned}$$

These sets are utilized in the model generation stage to define initial MFs in Sect. 3.2.1; the initial MFs of the three variables are shown in the left column of Fig. 8. In the following of the second stage, all rules are extracted from the data samples of the Ele-1 dataset using the rule generation process of Sect. 3.2.2; these rules and their weights are represented in Table 14. After that, the rule pruning method, 3.2.3, is carried out, and among the 7 generated rules, just 3 of them (the highlighted ones in italic) remain in the RB. Table 15 shows the structure of final RB.

Table 15 Results of rule pruning and rules’ weights adjustment

Finally, as Sect. 3.3, the post-processing tasks including MFs tuning and rules’ weights adjustment are done. The right column of Fig. 8 shows the MFs of the three variables after the tuning. Also, the optimized weights of the final rules are indicated in the weight column of Table 15. The test error of this run of EEFR-R for the Ele-1 dataset was equal to 2.0527E+05.

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Aghaeipoor, F., Eftekhari, M. EEFR-R: extracting effective fuzzy rules for regression problems, through the cooperation of association rule mining concepts and evolutionary algorithms. Soft Comput 23, 11737–11757 (2019).

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  • Discretization
  • Fuzzy rule learning
  • Rule pruning
  • Support and confidence
  • Particle swarm optimization