CN115712810A - Support vector logistic regression method with accurate prediction factor and storage medium - Google Patents

Support vector logistic regression method with accurate prediction factor and storage medium Download PDF

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CN115712810A
CN115712810A CN202211369584.0A CN202211369584A CN115712810A CN 115712810 A CN115712810 A CN 115712810A CN 202211369584 A CN202211369584 A CN 202211369584A CN 115712810 A CN115712810 A CN 115712810A
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support vector
logistic regression
regression
fuzzy
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孙梦思
琚春华
鲍福光
谷海彬
陈锦鹏
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Zhejiang Gongshang University
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Abstract

The application relates to the technical field of supervised learning in data mining, and discloses a support vector logistic regression method with accurate prediction factors, a storage medium and electronic equipment, wherein the support vector logistic regression method comprises the following steps: building an input dataset x i A matrix A of (A); constructing a non-linear prediction function based on the matrix A
Figure DDA0003924425190000011
According to the above-mentioned non-linear prediction function
Figure DDA0003924425190000012
Constructing three support vector regression models; carrying out model parameter solution on the three support vector regression models; error checking and predicting the three support vector regression modelsThe performance test can be used for processing the complicated nonlinear logistic regression problem by a simple and quick algorithm, and the fuzzy support vector logistic regression model provided by combining the accurate prediction variable and the fuzzy response has higher goodness of fit standard, reduces the influence of an isolated point on fuzzy prediction and improves the prediction precision.

Description

Support vector logistic regression method with accurate prediction factor and storage medium
Technical Field
The application relates to the technical field of supervised learning in data mining, in particular to a support vector logistic regression method with an accurate prediction factor and a storage medium.
Background
Data mining is a decision support process based on statistics, machine learning, artificial intelligence and the like, and is widely applied to the fields of business management, market analysis, production control, engineering design and the like through information and knowledge acquired after data mining along with the development of cloud computing; among them, logistic Regression (LR) is frequently used as a generalized regression analysis model in the fields of data mining, automatic disease diagnosis, economic prediction, and the like. However, in most real-life scenarios, the response variable of the regression analysis is a fuzzy quantity, rather than the traditional binary variable, and the use of traditional Logistic Regression (LR) is greatly constrained.
With the continuous and deep fuzzy mathematics research, the fuzzy logistic regression model is rapidly developed in the practical application of fuzzy data. However, none of the fuzzy logistic regression models in the past can make a major breakthrough in the validity and accuracy of the results, and cannot be well applied to processing the fuzzy phenomenon in life. The method relies on the traditional linear logistic regression, and uses the traditional optimization technology, such as least square error (LMSE), least Absolute Deviation (LAD) and other algorithms to estimate the components of the model, and is easily influenced by local extremum. Meanwhile, neglecting that the observed data can be modeled by mapping a function, the function may be a problem of nonlinear combination of model parameters and predicted values.
Disclosure of Invention
The application aims to overcome the defects of the prior art and provide a support vector logistic regression method with accurate prediction factors and a storage medium.
In a first aspect, a support vector logistic regression method with precise predictors is provided, including:
constructing an input dataset x i A matrix A of (A);
constructing a non-linear prediction function based on the matrix A
Figure BDA0003924425170000011
Wherein the content of the first and second substances,
Figure BDA0003924425170000012
and
Figure BDA0003924425170000013
is a blurring coefficient;
according to the above-mentioned non-linear prediction function
Figure BDA0003924425170000014
Constructing three support vector regression models;
carrying out model parameter solution on the three support vector regression models;
and carrying out error test and prediction performance test on the three support vector regression models.
Further, the constructing the matrix a of the input data comprises the following steps:
constructing a noise-corrupted training set
Figure BDA0003924425170000021
Wherein x is i ∈R n ，x i Corresponding observed value
Figure BDA0003924425170000022
Figure BDA0003924425170000023
The likelihood of success for the ith observation;
will input the value x i Arranged in a matrix A of n rows and m columns, wherein the value of the ith row is
Figure BDA0003924425170000024
Will be provided with
Figure BDA0003924425170000025
As a vector of observation blur values.
Further, the constructing of the non-linear prediction function
Figure BDA0003924425170000026
The method comprises the following steps:
obtaining a kernel matrix K (A, A) t ) So that
Figure BDA0003924425170000027
(K(A,A t )) ij ＝K(x i ,x j )；
In response to x ∈ R m Then, K (x, A) is added t )＝(K(x,x 1 ),…,K(x,x m ) Is a row vector;
constructing a non-linear prediction function
Figure BDA0003924425170000028
Figure BDA0003924425170000029
Figure BDA00039244251700000210
Wherein, w = (w) 1 ,…,w m ) T ，l w ＝(l w1 ,l w2 …,l wm ) T ，r w ＝(r w1 ,r w2 …,r wm ) T ；
Figure BDA00039244251700000211
Further, the construction of the support vector regression model comprises the following steps:
in response to K (A, A) t ) Is positive, then the above-mentioned nonlinearity is obtainedPrediction function
Figure BDA00039244251700000212
Equivalent to (f (x); l f(x) ；r f(x) ) T ＝(K(x,A t )w+b；K(x,A t )l w +l b ；K(x,A t )r w +r b ) LR To derive three support vector regression models:
v＝f(x)＝K(x,A t )w+b (4)
l v ＝l f(x) ＝K(x,A t )l w +l b (5)
r v ＝r f(x) ＝K(x,A t )r w +r b (6)。
further, the model parameter solution comprises the following steps:
fuzzy coefficients are optimized by adopting a three-stage algorithm;
analyzing a scatter plot-based fuzzy response estimate when an objective function value is minimal
Figure BDA00039244251700000215
Figure BDA00039244251700000213
Figure BDA00039244251700000214
Figure BDA0003924425170000031
Estimating the values of unknown coefficients and regression components of the common support vector regression model;
search through an exponential grid in the set 10 -5 ,10 -4 ,…,10 4 ,10 5 Searching regularization parameters c, c in the support vector algorithm l ,c r To obtain an improved support vector regression model.
Further, the kernel function K (x) used in the three-stage optimization algorithm is an Epanechnikov kernel function, and the expression thereof is as follows:
Figure BDA0003924425170000032
further, the error checking includes:
the root-mean-square error is checked,
Figure BDA0003924425170000033
the average absolute relative error is checked and compared with the standard,
Figure BDA0003924425170000034
the similarity measure is checked and used for checking,
Figure BDA0003924425170000035
where, n, u, denotes the intersection and union on the fuzzy number space, and Card (S) denotes the number of elements in the finite set S.
Further, the predicting performance detection comprises:
fuzzy response to fuzzy by Sugeno fuzzy model
Figure BDA0003924425170000036
And a scatter plot based fuzzy response estimate
Figure BDA0003924425170000037
Defuzzification of the relationship between them to obtain the corresponding accurate value
Figure BDA0003924425170000038
And
Figure BDA0003924425170000039
when in use
Figure BDA00039244251700000310
And
Figure BDA00039244251700000311
the closer the values of (a) are, the higher the predictive performance of the model.
In a second aspect, the computer readable medium stores program code for execution by a device, the program code comprising instructions for performing the steps of the method according to any one of the implementations of the first aspect.
In a third aspect, an electronic device is characterized in that the electronic device includes a processor, a memory, and a program or an instruction stored on the memory and executable on the processor, and when executed by the processor, the program or the instruction implements the steps of the method according to any one of the implementations of the first aspect.
The application has the following beneficial effects: the support vector logistic regression method based on the kernel function can process complex nonlinear logistic regression problems through a simple and rapid algorithm, combines an accurate prediction variable and a fuzzy support vector logistic regression model provided by fuzzy response, has a relatively high goodness-of-fit standard, reduces the influence of an isolated point on fuzzy prediction, and improves the prediction precision by measuring the classification confidence coefficient.
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The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application, and are incorporated in and constitute a part of this application for purposes of illustration and description.
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart of a support vector logistic regression method with accurate predictors according to a first embodiment of the present application;
FIG. 2 is a statistical chart of 100 subjects listed in the support vector logistic regression method with accurate predictors according to the first embodiment of the present application;
FIG. 3 is a diagram of a fuzzy language term set in a support vector logistic regression method with accurate predictors according to a first embodiment of the present application;
FIG. 4 is a diagram illustrating the detection of outliers in the SVM regression with accurate predictors method according to the first embodiment of the present application
Figure BDA0003924425170000041
The scatter plot of (a);
FIG. 5 is a schematic diagram of a support vector logistic regression method with accurate predictors according to a first embodiment of the present application
Figure BDA0003924425170000042
And
Figure BDA0003924425170000043
comparison plots of values with other fuzzy regression methods;
FIG. 6 is a fuzzy language term set and its corresponding statistical chart in the support vector logistic regression method with accurate predictor according to the first embodiment of the present application;
FIG. 7 is a graph of the estimated fuzzy coefficients of the model and their performance metrics corresponding to the proposed method and some common fuzzy logistic regression models.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment of the application relates to a method for predicting a factor with accuracyA support vector logistic regression method comprising: building an input dataset x i A matrix A of (A); constructing a non-linear prediction function based on the matrix A
Figure BDA0003924425170000051
According to the above non-linear prediction function
Figure BDA0003924425170000052
Constructing three support vector regression models; carrying out model parameter solution on the three support vector regression models; and carrying out error test and prediction performance test on the three support vector regression models.
Specifically, fig. 1 shows a flowchart of a support vector logistic regression method with accurate predictors in the first application embodiment, which specifically includes the following steps:
s101, constructing an input data set x i A matrix A of (A);
specifically, the constructing of the matrix a of the input data includes the following steps:
constructing a noise-corrupted training set
Figure BDA0003924425170000053
Wherein x is i ∈R n ，x i Corresponding observed value
Figure BDA0003924425170000054
Figure BDA0003924425170000055
The likelihood of success for the ith observation;
will input the value x i Arranged in a matrix A of n rows and m columns, wherein the value of the ith row is
Figure BDA0003924425170000056
Will be provided with
Figure BDA0003924425170000057
As a vector of observation blur values.
Illustratively, the age (in years) of 100 subjects and the presence or absence of evidence of a potential purchase are listed in FIG. 2, which also contains an identification variable ID and an age group variable x 2 Obtaining corresponding fuzzy response observation vector by calculation
Figure BDA0003924425170000058
Thereby obtaining a noise-corrupted training set
Figure BDA0003924425170000059
Wherein for each input value x i ∈R n Its corresponding observed value
Figure BDA00039244251700000510
Result variable
Figure BDA00039244251700000511
For "purchase", encoding is performed according to some linguistic terms, such as Very Low (VL), low (SL), low (L), slightly low (ALL), medium (M), slightly high (ALH), high (H), some High (SH), and Very High (VH), as shown in FIG. 2, with membership functions as shown in FIG. 3, and a set of ambiguous linguistic terms and their corresponding ones
Figure BDA00039244251700000512
As shown in fig. 6.
S102, constructing a nonlinear prediction function based on the matrix A
Figure BDA00039244251700000513
Illustratively, to simplify the representation and processing of fuzzy numbers, an LR-type fuzzy number is used here
Figure BDA00039244251700000514
The parametric form of the function of (2) is defined as follows:
Figure BDA00039244251700000515
wherein a is ∈ R, l a (>0) And r a (<0) Are respectively called
Figure BDA00039244251700000516
Is a mean, left-right spread, function L (or R) at R + →[0,1]L (0) =1,L (1) =0, L (x) monotonically decreases, and further, inaccuracy in the data set is dealt with by the most common triangular blur number in the LR model, i.e., L (x) = R (x) = max {0,1, -x }, so L (0) = 5363 (1) =0, L (x) monotonically decreases, and further, inaccuracies in the data set are dealt with, thereby
Figure BDA0003924425170000061
Can be further expressed as follows:
Figure BDA0003924425170000062
at the same time, when
Figure BDA0003924425170000063
Then, some operations on fuzzy sets are defined as follows:
Figure BDA0003924425170000064
Figure BDA0003924425170000065
specifically, a kernel function K (x) is introduced, and an m-order kernel matrix K (A, A) is defined t ) So that
Figure BDA0003924425170000066
(K(A,A t )) ij ＝K(x i ,x j ) Then for any x ∈ R m All have K (x, A) t )＝(K(x,x 1 ),…,K(x,x m ) Is a row vector, therefore, a non-linear prediction function is assumed
Figure BDA0003924425170000067
Figure BDA0003924425170000068
Wherein the content of the first and second substances,
Figure BDA0003924425170000069
and
Figure BDA00039244251700000610
blur coefficients (unknown):
Figure BDA00039244251700000611
Figure BDA00039244251700000612
wherein, w = (w) 1 ,…,w m ) T ，l w ＝(l w1 ,l w2 …,l wm ) T ，r w ＝(r w1 ,r w2 …,r wm ) T ；
Figure BDA00039244251700000613
S103, according to the nonlinear prediction function
Figure BDA00039244251700000614
Constructing three support vector regression models;
in particular, since the kernel matrix K (A, A) t ) If the number is positive, the above-mentioned non-linear prediction function
Figure BDA00039244251700000615
Equivalent to (f (x); l f(x) ；r f(x) ) T ＝(K(x,A t )w+b；K(x,A t )l w +l b ；K(x,A t )r w +r b ) LR Therefore, the following three support vectors can be deducedClassification (SVR) model:
v＝f(x)＝K(x,A t )w+b (4)
l v ＝l f(x) ＝K(x,A t )l w +l b (5)
r v ＝r f(x) ＝K(x,A t )r w +r b (6)
thus, there is a set of training sets
Figure BDA0003924425170000071
The unknown blurring coefficients can be found by the following three-stage method
Figure BDA0003924425170000072
And
Figure BDA0003924425170000073
s104, solving model parameters of the three support vector regression models;
specifically, the unknown blurring coefficient is obtained
Figure BDA0003924425170000074
And
Figure BDA0003924425170000075
and (3) carrying out three-stage optimization algorithm evaluation on the fuzzy coefficient, namely analyzing the corresponding fuzzy response estimated value based on the scatter diagram by using a support vector method (applying Epanechnikov kernel function) on the three obtained Support Vector Regression (SVR) models
Figure BDA0003924425170000076
Figure BDA0003924425170000077
Figure BDA0003924425170000078
Figure BDA0003924425170000079
Wherein, C l ,C r >0 is a regularization constant, L H Represents the absolute error loss function, defined as follows:
Figure BDA00039244251700000710
the Support Vector Machine (SVM) can minimize the structural risk through a three-stage optimization algorithm to generalize the limitation of the sum of training errors so as to approximate the functional relationship in regression analysis. Compared with a traditional nonlinear regression model, support Vector Regression (SVR) can combine classifiers trained on different types of data by applying probability rules on the robustness of abnormal values; and the prediction precision is improved by measuring the classification confidence.
Estimating unknown coefficients and regression components of a common SVR model with mathematical software and searching in the set 10 using an exponential grid -5 ,10 -4 ,…,10 4 ,10 5 Searching regularization parameters c, c in the support vector algorithm l ,c r To obtain an improved Support Vector Regression (SVR) model, the results are shown in table 1.
Table 1:
Figure BDA00039244251700000711
Figure BDA0003924425170000081
Figure BDA0003924425170000091
Figure BDA0003924425170000101
the kernel function K (x) used in the three-stage optimization algorithm is an Epanechnikov kernel function, and the expression of the kernel function K (x) is as follows:
Figure BDA0003924425170000102
and S105, carrying out error test and prediction performance test on the three support vector regression models.
Specifically, the error checking includes:
the Root Mean Square Error (RMSE) test,
Figure BDA0003924425170000103
the Mean Absolute Relative Error (MARE) test,
Figure BDA0003924425170000104
a similarity metric (MSM) test,
Figure BDA0003924425170000105
where —, u, denotes the intersection and union on the Fuzzy Number (FN) space, the number of elements in the Card (S) finite set S, fig. 7 summarizes the values of the estimated fuzzy coefficients associated with each kernel and their goodness-of-fit criteria; in addition, the estimated fuzzy coefficients and performance metrics associated with some common fuzzy logistic regression models (Gao and Lu, pourahmad et al, and Namdari et al) are listed in FIG. 2.
The predictive performance detection includes:
responding to blur by Sugeno blur model
Figure BDA0003924425170000106
And a scatter plot based fuzzy response estimate
Figure BDA0003924425170000107
Defuzzification of the relationship between them to obtain the corresponding accurate value
Figure BDA0003924425170000108
And
Figure BDA0003924425170000109
wherein:
Figure BDA00039244251700001010
when in use
Figure BDA00039244251700001011
And
Figure BDA00039244251700001012
the closer the values of the two are, the higher the prediction performance of the model is, and an expanded Cock distance criterion is adopted to check outliers, so that the outliers are used
Figure BDA00039244251700001013
If the accuracy of the results is higher, indicating that FLOGSVR provides more accurate results, the method may enter a method implementation stage, for which the data set contains some potential outliers, as observed in fig. 4, and compare the method with other fuzzy regression models, as shown in fig. 5 (where fig. 5 includes fig. 5a, fig. 5b, fig. 5c, and fig. 5 d), it may be concluded that: the values of M estimated with the proposed algorithm are closer to their respective estimated values, and these figures also show that the performance of the proposed fuzzy logistic regression model is better than other models.
The Support Vector Regression (SVR) model with accurate prediction variables and fuzzy response is introduced into a nonlinear (based on a common kernel function) logistic regression model, the support vector logistic regression method with the accurate prediction factors is provided, the support vector logistic regression method based on the kernel function can process complex nonlinear logistic regression problems through a simple and rapid algorithm, the fuzzy support vector logistic regression model provided by combining the accurate prediction variables and the fuzzy response has a relatively high goodness of fit standard, the influence of isolated points on fuzzy prediction is reduced, and the prediction precision is improved by measuring the classification confidence.
Example two
A computer-readable storage medium according to a third embodiment of the present application, storing program code for execution by a device, the program code including instructions for performing a method according to any one of the first to third embodiments of the present application;
the computer readable storage medium may be a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM); the computer readable storage medium may store program code for performing the steps of the method as in any one of the implementations of the embodiment of the present application when the program stored in the computer readable storage medium is executed by the processor.
EXAMPLE III
A chip related to a fourth embodiment of the present application, where the chip includes a processor and a data interface, and the processor reads an instruction stored in a memory through the data interface to execute the steps of the method in any one implementation manner in the first embodiment of the present application;
the processor may adopt a general Central Processing Unit (CPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), a Graphics Processing Unit (GPU), or one or more integrated circuits, and is configured to execute a related program, so as to implement the method in any implementation manner in the first embodiment of the present application.
The processor may also be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the method in any one implementation of the first embodiment of the present application may be implemented by hardware integrated logic circuits in a processor or instructions in the form of software.
The processor may also be a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), an FPGA (field programmable gate array) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory, and performs, in combination with hardware of the storage medium, functions required to be performed by a unit included in the data processing apparatus according to the embodiment of the present application, or performs a method according to any one implementation manner of the embodiment of the present application.
The above are only preferred embodiments of the present application; the scope of protection of the present application is not limited thereto. Any person skilled in the art should be able to cover all equivalent or changes within the technical scope of the present disclosure, which is equivalent to the technical solution and the improvement concept of the present disclosure, and the protection scope of the present disclosure.

Claims (10)

1. A method of support vector logistic regression with accurate predictors, comprising:
building an input dataset x i A matrix A of (A);
constructing a non-linear prediction function based on the matrix A
Figure FDA0003924425160000011
Wherein the content of the first and second substances,
Figure FDA0003924425160000012
and
Figure FDA0003924425160000013
is a blurring coefficient;
according to the above-mentioned non-linear prediction function
Figure FDA0003924425160000014
Constructing three support vector regression models;
carrying out model parameter solution on the three support vector regression models;
and carrying out error test and prediction performance test on the three support vector regression models.
2. The method of support vector logistic regression with accurate predictors according to claim 1, wherein said constructing a matrix a of input data comprises the steps of:
constructing a noise-corrupted training set
Figure FDA0003924425160000015
Wherein x is i ∈R n ，x i Corresponding observed value
Figure FDA0003924425160000016
Figure FDA0003924425160000017
The likelihood of success for the ith observation;
will input the value x i Arranged in a matrix A of n rows and m columns, wherein the value of the ith row is
Figure FDA0003924425160000018
Will be provided with
Figure FDA0003924425160000019
As a vector of observation blur values.
3. The method of claim 1, wherein the constructing a non-linear prediction function
Figure FDA00039244251600000110
The method comprises the following steps:
obtaining a kernel matrix K (A, A) t ) So that
Figure FDA00039244251600000111
(K(A,A t )) ij ＝K(x i ,x j )；
In response to x ∈ R m Then, K (x, A) is added t )＝(K(x,x 1 ),…,K(x,x m ) Is a row vector;
constructing a non-linear prediction function
Figure FDA00039244251600000112
Figure FDA00039244251600000113
Figure FDA00039244251600000114
Wherein, w = (w) 1 ,…,w m ) T ，l w ＝(l w1 ,l w2 …,l wm ) T ，r w ＝(r w1 ,r w2 …,r wm ) T ；
Figure FDA00039244251600000115
4. The support vector logistic regression method with accurate predictor according to claim 1, wherein the construction of the support vector regression model comprises the following steps:
in response to K (A, A) t ) If the number is positive, the above-mentioned non-linear prediction function
Figure FDA00039244251600000116
Equivalent to (f (x); l f(x) ；r f(x) ) T ＝(K(x,A t )w+b；K(x,A t )l w +l b ；K(x,A t )r w +r b ) LR To derive three support vector regression models:
v＝f(x)＝K(x,A t )w+b (4)
l v ＝l f(x) ＝K(x,A t )l w +l b (5)
r v ＝r f(x) ＝K(x,A t )r w +r b (6)。
5. the method of support vector logistic regression with accurate predictors according to claim 1, wherein said model parameter solution comprises the steps of:
fuzzy coefficients are optimized by adopting a three-stage algorithm;
analyzing a scatter plot-based fuzzy response estimate when an objective function value is minimal
Figure FDA0003924425160000021
Figure FDA0003924425160000022
Figure FDA0003924425160000023
Figure FDA0003924425160000024
Estimating the values of unknown coefficients and regression components of the common support vector regression model;
search through an exponential grid in the set 10 -5 ,10 -4 ,…,10 4 ,10 5 Searching regularization parameters c, c in the support vector algorithm l ,c r To obtain an improved support vector regression model.
6. The support vector logistic regression method with accurate predictor according to claim 5, wherein the kernel function K (x) used in the three-stage optimization algorithm is Epanechnikov kernel function, and the expression thereof is as follows:
Figure FDA0003924425160000025
7. the method of support vector logistic regression with accurate predictors according to any of claims 1-6, wherein said error checking comprises:
the root-mean-square error is checked,
Figure FDA0003924425160000026
the average absolute relative error is checked and compared with the standard,
Figure FDA0003924425160000027
the similarity measure is checked and used for checking,
Figure FDA0003924425160000028
where, n, U, denotes the intersection and union over the fuzzy number space, and Card (S) denotes the number of elements in the finite set S.
8. The method of support vector logistic regression with accurate predictors according to any of claims 1-6, wherein said prediction performance detection comprises:
responding to blur by Sugeno blur model
Figure FDA0003924425160000031
And a scatter plot based fuzzy response estimate
Figure FDA0003924425160000032
Defuzzification of the relationship between them to obtain the corresponding accurate value
Figure FDA0003924425160000033
And
Figure FDA0003924425160000034
when in use
Figure FDA0003924425160000035
And
Figure FDA0003924425160000036
the closer the values of (a) are, the higher the predictive performance of the model.
9. A computer-readable storage medium, characterized in that the computer-readable medium stores program code for execution by a device, the program code comprising steps for performing the method according to any one of claims 1-8.
10. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing the steps of the method according to any one of claims 1-8.
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