Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making

. The expert is a vital role in multicriteria decision-making, which provides source decision opinions. In the existing group decision-making activities, the selection of experts is usually conducted artiﬁcially, which relies on personal subjective experience. It has been the urgent demand for an automatic selection of experts, which can help to determine their weights for the follow-up decision calculation. In this paper, an expert classiﬁcation method is proposed to solve the problem. First, the CatBoost classiﬁcation algorithm is improved by integrating the 2-tuple linguistic, which can eﬀectively extract the features of samples. Second, the framework of the expert classiﬁcation is designed. The ﬂow combines the expert resume collection, expert clas-siﬁcation, and database update. Third, a decision-making case is analyzed for the expert selection issue. The experiment and result indicate that the proposed classiﬁer performs better than the classic methods. The proposed classiﬁcation method of the decision experts can support the automatic and intelligent operation of the decision-making activities.


Introduction
Decision-making is a vital activity in various management works. It is a complex process of thinking and operation [1]. Rational decision-making has become an important part of the modern management theory, in which the decision-making always involves multicriteria group decision-making [2]. e decision-making methods originate from the persons' thinking process. erefore, the essential basis is the experts' information and knowledge. e expert here means the administrator, decision-maker, and scholar in the related field. e professional and adaption level of experts affects the decision-making greatly. How to select the appropriate decision experts has been a novel issue.
Moreover, the computer and information technology bring the automatic decision support system (DSS) [3,4]. In the automatic decision-making programs like the DSS, the expert information is collected and stored, such as the professional title, educational background, professional background, and research field. It provides the opportunity to select the decision experts automatically and rationally.
Specifically, the selection of experts is usually done manually, which relies on the subjective judgment of administrators. e selection of experts is not objective enough so that the results are often not persuasive. e artificial approach is unreliable and inefficient in the actual operation. Moreover, in the Internet information era, it is an available demand to automatically analyze the expert information and select the appropriate ones for the decision-making [5]. e experts should be analyzed with their research level and practical experience. en the experts can be classified and selected according to their professional grades and the decision-making situation. e core issue can be abstracted as the classification and evaluation of the experts. e typical classification algorithms of machine learning include naive Bayes, k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), and logistic regression [6][7][8][9]. It develops with the integrated learning algorithms such as random forest, AdaBoost, XgBoost, and CatBoost [10,11]. Meanwhile, other machine learning methods are also studied for feature extraction [12][13][14]. e input of the classifiers may be a real number, linguistic text, and grade variable. For the fuzzy inputs, they are usually converted with the one-hot coding method [15]. e simple conversion may lose the detailed and hidden information of the categorical features [16][17][18], which makes it impossible to accurately and completely express the information carried by the original data.
Given the demands above, the experts should be analyzed automatically with the machine learning, in which the problems of information coding and feature recognition should be solved. is paper proposes an expert classification method from the perspective of objectivity and intelligent computing. e main work is briefly presented as follows.
(i) e 2-tuple linguistic [19] is introduced into the integrated classifier CatBoost [20] to fully express the fuzzy features and classify the experts. (ii) e general framework is designed with the fuzzy classification model. It can be used to select decision experts and update the expert library. (iii) e ability of feature extraction and classification is tested with the one-hot encoding and traditional classifiers. Results show that CatBoost integrated with 2-tuple linguistic can effectively recognize the experts and help evaluate and select the decision expert in an automatic solution.
is paper is organized as follows. Section 2 introduces the works related to the expert classification and decisionmaking. Section 3 expounds on the proposed method of expert classification. Section 4 carries out a case of the expert classification for the decision-making in water environment pollution. e method and results are discussed in Section 5, and the paper is concluded finally in Section 6.

Group Decision-Making and Decision Support System.
e core thought of rational decision-making is evaluating the alternatives from multiple aspects and persons. e multicriteria group decision-making realizes the thought, and it will be introduced firstly in this section.
Decision-making is the process of judging a decision problem or finding a solution. In general, a satisfactory solution should be most acceptable by the entire administrators. erefore, the group decision method is proposed to improve the traditional individual method. e group decision-making mainly uses the core theory of the multicriteria and multiattribute decision-making [21,22], which involves the integration of the opinions of multiple experts and the weight calculation of alternative scheme attributes. In most situations, the decision opinions are given in the form of linguistic text by the group of decision experts. In order to aggregate the linguistic information, Xu [23] proposed a group decision-making method based on the relation of language preference. Qian et al. [24] redefined some basic operations of the generalized hesitant fuzzy set and presented a group decision-making method based on the improved hesitant fuzzy set. Chen [25] extended the technique for order preference by similarity to an ideal solution (TOPSIS) to the fuzzy decision environment. Besides, the decision-making methods are extended in the neutrosophic set environment [26][27][28], which try to promote the information expression level. More types of group decision-making are explored, such as the methods based on granular computing [29][30][31].
ey aim at the decisionmaking with multiple dimensions.
With information technology development, the different decision-making methods can be realized in the DSS. Since Gorry [32] first put forward the concept of decision-making with the software system, the DSS has been studied and developed. Up to now, DSS has been widely used, including clinical DSS in hospitals, environmental DSS in government departments, and DSS in urban water supply management [33]. e DSS mainly consists of three parts: data access and operation, model based on data, and case matching. With the development of computer and information technology, the DSS develops in various solutions [34,35]. e DSS has provided an automatic platform to run the decision-making methods.
It can be seen that multicriteria group decision-making is built based on decision experts. e experts need to be selected to express decision opinions [36]. e existing studies on group decision-making generally focus on the integration of experts' decision opinions. However, the analysis of experts themselves is not sufficient, and the level differences among experts are not fully considered. Besides, the expert library is an important module of the DSS. Most of the systems collect and evaluate the experts in the manual solution. Meanwhile, the system pays more attention to the case library and other knowledge bases, instead of the expert library [37]. It can be concluded that the selection and evaluation of decision experts directly affect the reliability of decision results. Moreover, the modern and intelligent DSS has an urgent need for automatic analysis and classification of experts. erefore, it is necessary to explore the automatic method of the expert classification.

Classification Methods in Machine Learning.
ere are many kinds of classification algorithms in machine learning. e classifiers are widely used in data mining, text recognition, and other fields.
As mentioned in Introduction, the classical algorithms include naive Bayes, KNN, DT, SVM, and logistic regression [6][7][8][9]. e integrated learning algorithms include random forest, AdaBoost, XgBoost, and CatBoost [10,11,20]. However, the classifiers themselves do not provide a mechanism for input features. e sample data are usually converted into numerical data by one-hot encoding before training models. e hidden information may be lost by the categorical feature conversation with one-hot encoding, which will impact the classification accuracy [16].
2 Complexity e CatBoost algorithm has the characteristics of high classification accuracy and fast training speed. It has been the latest classifier which is of strong learning ability. Moreover, CatBoost provides a mechanism of converting input features into numerical values. However, the conversation method of CatBoost is not suitable for expert sets with small samples, especially for the samples with a few input features. For the problem of expert classification, the integrated method represented by CatBoost can provide an effective learning technique, but it should be remolded for the input feature conversation.
For the feature extraction of input data, some fuzzy analysis methods can provide the basic technique. e classical theories include the fuzzy set and rough set [38,39], in which the degree of information uncertainty is expressed with the probability and membership function. e fuzzy analysis methods are developed in the demand for language comprehension. e grade variables in the text can be transformed and calculated with the vague set [40] and 2tuple linguistic [19,41]. e related studies show that the 2tuple linguistic is flexible and effective to express the rating grade information, of which the calculation load is lower than the complex natural language processing technique.
Significantly different from previous studies, we will propose an intelligent method to create the decision expert library. Our innovative contributions are highlighted as follows: (1) e automatic classification and evaluation of decision experts are mainly focused. An improved fuzzy CatBoost classifier is proposed, integrated with 2-tuple linguistic. e information of experts is expected to be extracted more soundly and deeply.
(2) e creative flow of the expert classification and selection is designed based on the improved classifier, which can help the application of group decision-making in the software programming of the DSS.

Problem Description.
e existing research indicates that the group multicriteria decision method depends on the selection and aggregation of experts. It is the classification problem of decision experts. e reasonable experts should be selected considering as many characteristics of experts as possible, such as the research field, academic level, and practical experience. Also, the representation of expert information should be easy and available for computer computation. e information collection and analysis calculation should be realized with the software program. Considering the demand, the problem is abstracted and presented as follows.
e fundamental task for expert classification is the importing of the expert information. In the task, the experts are selected and classified to form different categories. First, the resumes of experts are collected, and the information will be processed uniformly. In order to meet the requirements of machine learning, the input data of experts should be objective enough. is paper uses structured data to represent the attributes of experts. ere are n experts, and the expert set is represented as E, E � e 1 , e 2 , . . . , e n . Each expert is described with m attributes, and the attribute is represented as where k is the serial number of the experts. e attribute variable is in different forms according to the property information. One of the attribute variables is the numerical value, while others are represented with texts. e attribute variable in the text form should be converted for the unified input of the classifier. Moreover, for the elements in the expert set, they are corresponding to the category label. e category means the general evaluation of the expert level, in view of the academic level, the professional degree in the field, and the quantized achievements. e category label is represented by y k , where y k � 1, 2, 3, . . . { } and the number of categories can be determined in the concrete decision-making situation. e main task in the paper is to build the classifier that can model the relation between the attribute set x k and the category label y k . e data processing and classification method are introduced in Section 3.2.

CatBoost Algorithm Integrated with 2-Tuple Linguistic.
For the advanced integrated classification method, the CatBoost algorithm is embedded with a processing mechanism of input features. e processing replaces the original inputs of numerical codes. e main solution of CatBoost is as follows.
e attribute variable x i k represents the i-th dimensional input feature of the k-th training sample. e category label of the k-th sample is y k . e conditional expected value of x i k under the category y k is expressed as x j k ; namely, as a training sample, and σ is a random arrangement. x i k is calculated as where p is the smoothing parameter and it is usually set to the mean value of the target data set. a is the coefficient of p, and it is greater than 0. e sign 1 x i j �x i k means the function of which its value is 1 if the marked variables are equal. CatBoost uses different permutations in the steps of gradient enhancement. e classical algorithm of CatBoost above has been applied in various classification issues. However, it does not work well for the small samples with its built-in processing tool of input features, as shown in the example analysis of Section 4.2. Moreover, some inputs are in the text form with fuzzy information. en 2-tuple linguistic is introduced as the analysis tool for the fuzzy text variable. e variable of 2-tuple linguistic can express the grade and membership degree of an object. It is expressed in the form of (s i , a i ).
e symbol s i is the i-th element of the predefined grade set s. a i is the sign transfer value: a i ∈ [− 0.5, 0.5), indicating the deviation between the real evaluation degree and s i .

Complexity
For the transform of the common input attribute to the 2-tuple linguistic, different measures can be taken in view of the input format. If the input attribute is expressed with a defined grade set, namely, s i ∈ S, the 2-tuple linguistic can be obtained directly with the function θ: If the input attribute is expressed with a real number β ∈ [0, T], T is the number of elements in the grade evaluation set S. round is the rounding operator. β is transformed into the 2-tuple linguistic variable by the function Δ.
e function Δ is defined as Conversely, the 2-tuple linguistic (s i, a i ) can be converted to β by the inverse function Δ − 1 : In the proposed classifier, the built-in input feature processing tool in CatBoost is replaced with the 2-tuple linguistic. e CatBoost algorithm integrated with 2-tuple linguistic is proposed as the expert classifier. e improved method is abbreviated as 2L-CatBoost.
In the method, the inputs are preprocessed according to their formats. If the inputs are expressed with the grade set in text format, they can be transformed into 2-tuple linguistic following formulas (2) and (3). If the inputs are given with numbers, they can be transformed following formulas (4) and (5). Besides, the membership degree of a i can be given by the calculation of semantic similarity. en the processed inputs can be imported into the CatBoost, and the CatBoost algorithm is shown in Algorithm 1.

Automatic Flow of Expert Classification and Selection.
e experts should be analyzed and selected reliably for multicriteria group decision-making. Especially in modern DSS, the work should be operated with the software. en an automatic flow of the expert classification and selection is designed, in which the collection and update of the expert information are also contained. e experts are classified by the 2L-CatBoost algorithm. e classification results can be the main reference for the expert selection, and the selected experts can provide decision opinions for the multicriteria decision-making activities.
e flow of the expert classification and selection is shown in Figure 1.
As shown in Figure 1, the process of expert classification and selection consists of two parts, namely, the training and application. In the figure, the training part is shown with the green blocks. e application part is blue, and the shared part is dark orange. e concrete flow can be summarized as follows: (1) e expert information is collected with the web crawler or artificially. e information should meet the needs as much as possible; that is, the information can well reflect the academic level and experience degree of experts in a certain field. (2) e information of experts needs to be stored with structured data, which can be used to train the classifier model and facilitate users to consult expert information. erefore, if the data source contains semistructured data, they will be converted into structured data with 2-tuple linguistic. e flow above is designed for the automatic building and updating of the expert library in DSS. In the process of group decision-making, the anticipant experts can be selected by the system according to the category result. e subsequent decision-making activities can be executed by other modules in the DSS. With the operation of DSS, the data in the expert library will increase, which can help to improve and evolve the system and library based on the proposed classification learning method. en it can help the DSS to obtain the strong ability of automatic decisionmaking in the mode of human thinking.

Data and Experiment Setting.
e proposed classification method for the decision expert selection is verified with a decision-making case. In the previous studies [42,43], we have analyzed the decision-making of water pollution governance. For the pollution of algal bloom in the urban lakes, it is necessary to monitor and predict its trends by using some parameter estimation methods [44][45][46][47][48][49][50][51][52] such as the recursive algorithms [18,50,[53][54][55][56][57][58]. When it breaks or is going to break, rational decision-making should be carried out for emergency management. In order to promote the scientific and efficient governance of algal bloom, it is the first task to select and invite experts from the DSS. en the other decision-making activities can run based on the experts' decision opinion.
In the selection of experts, the administrators usually give priority to ones with high academic level and rich practical experience in the field. e classification and evaluation of experts will be tested in the experiment. e basic resumes of experts are collected in previous studies. e information is from the affiliated websites and China National Knowledge Infrastructure (CNKI). e collected expert information is stored in a structured datasheet. A total of 56 experts and scholars related to algal bloom management are collected [42]. Each expert is represented with 11 attribute variables that can reflect the academic level and professional experience. e names and meanings of attributes are shown in Table 1. e professional title is set as a categorical feature and other attributes are of a numerical value. e basic data set of experts is shown in Table 2. e experts in Table 2 are divided into five categories. 56 samples are tagged with the category labels of I, II, III, IV, and V. Level I means the expert is the highest professional, and V means the lowest. e distribution of the expert category is shown in Figure 2. For the demand for the minimum size of samples in the classifier training, the existing data set is expanded. e Monte Carlo simulation [59] is used to expand the data set to 50 samples in each    Table 3. e 2L-CatBoost is trained with the expanded samples. e parameters in the training are shown in Table 4.

Result.
e original 56 samples of experts are imported into the trained 2L-CatBoost classifier.
e classification results are shown in Table 5, where the value in the table means the number classified into the class of columns from the class of the row. e confusion matrix of the classification result is shown in Figure 3.
From the results in Table 5, it can be seen that only 3 of the 56 test samples are misclassified. e total classification accuracy is 94.64%. In the misclassified samples, 2 experts in class III are classified into class II; and 1 expert in class IV is classified into class III. e error can also be seen in Figure 3. e proposed 2L-CatBoost mainly improves the classifier by processing the input with 2-tuple linguistic. en the effect of the processing approach is tested and compared with the traditional ways, of which the one-hot encoding and the embedded feature conversation in CatBoost are set as the contrast. e samples of 56 experts are preprocessed with the three methods; then they are imported into the same Cat-Boost classifier. e classification accuracy of the three methods is shown in Figure 4.
For the different preprocessing methods, the proposed method obtains the best result. e result of the embedded feature conversation is the worst. It indicates that the mechanism embedded in CatBoost cannot effectively dispose of the samples in small size. Its accuracy is even worse than the one-hot encoding method.
Moreover, the classical classifiers are set as the contrast methods, including the SVM, DT, AdaBoost, random forest, naive Bayes, KNN, logistic regression, and XgBoost. For the SVM, the different kernel functions are used. e classification accuracy of the methods is shown in Figure 5. In the contrast experiments, two preprocessing approaches of the inputs are adapted, the one-hot encoding and the 2-tuple linguistic, represented with different colors in Figure 5. e results show that 2L-CatBoost has the highest classification accuracy.

Discussion
As the core information source of the multicriteria decisionmaking, the experts should be evaluated and selected rationally and automatically. In this paper, the classifier of machine learning is introduced to solve the problem. An improved fuzzy classifier 2L-CatBoost is proposed. e experiments are designed to test the performance. e proposed classifier is discussed based on the results.
For the proposed 2L-CatBoost classifier, it integrates CatBoost and the 2-tuple linguistic, which can take the advantages of both methods. For the CatBoost algorithm, it is proved to be effective in the expert classification. e proposed classifier has obtained a better result than other classifiers, including KNN, XgBoost, SVM, and naive Bayes. Besides, the training time of the 2L-CatBoost classifier is very short. In the experiment, it takes 7 iterations to obtain the Number of papers about the water environment Real number f9 Number of papers about chlorophyll Real number f10 Number of papers about blue-green algae Real number f11 Number of papers about aquatic organism Real number   e other main contribution is the processing of fuzzy inputs. e 2L-CatBoost classifier introduces the 2-tuple linguistic for the feature conversation of the input fuzzy information. e 2-tuple linguistic helps to increase the accuracy of the CatBoost classifier. Compared with the classical one-hot encoding method, the 2-tuple linguistic makes logistic regression, naive Bayes, and CatBoost better on the categorization. e results also show that    Complexity not all classifiers are suitable to use 2-tuple linguistic to deal with categorical features in the case of small samples. CatBoost has been proved to be effective in the case of a large sample size, but it fails when the data size is limited with the embedded feature processing. In this case, the proposed 2L-CatBoost still performs well with the help of the 2-tuple linguistic.
In summary, the intelligent classification method of decision experts based on machine learning proposed in this paper can effectively process expert information. After the construction of the expert information database, the task of expert classification can be completed. e process is objective enough, which is helpful to promote the standardization and efficiency of the decision-making process. e selection and classification of experts will help the subsequent group decision-making with multiple approaches [60,61]. e method proposed in this paper still has some shortcomings that need to be further improved in the followup work. e method has not been proved to be useful in any other data set. It is indeed difficult to adapt to all the data situations. e transferability of the method should be explored in the future. Besides, if the input data contains semistructured data, it needs the manual concertation from semistructured data to structured data, which undoubtedly increases the workload of users and reduces the efficiency. In the future, it is expected to explore the automatic way to convert the unstructured and semistructured data.

Conclusion
e issue of the decision source is studied for the multicriteria group decision-making. An automatic classification method of the experts is expected to be the important support for expert selection. In the solution, the improved fuzzy CatBoost classifier is proposed, integrated with the 2-tuple linguistic. It can dispose of the fuzzy input features effectively for accurate classification. Meanwhile, the general creation and update of the decision expert database are also designed. e experiment and results indicate that the proposed 2L-CatBoost is available and suitable for the expert classification with small samples and fuzzy inputs of features. In the future, more intelligent techniques can be introduced and studied to improve the fuzzy information in multicriteria decision-making, including automatic text collection and analysis, data prediction, and natural language understanding. en the multicriteria group decision-making method will be efficient and intelligent. e proposed method in this paper can combine other estimation algorithms [62][63][64][65] to study the multicriteria decisionmaking problems.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request. 64

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.