# Toward Labor Legal Risk Assessment Based on Unbiased Iterative Brunching Decision Tree Algorithm

Authors: Sui Yan
Journal: Mobile Information Systems
 (2022)

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## Abstract

In recent years, with the advancement of China’s labor laws and regulations, especially the strengthening of workers’ awareness of legal rights protection, enterprise labor risk is rising. It is essential for trade unions to help enterprises establish and improve the risk assessment mechanism of labor and employment on time, work hard from the four links of prediction, preexamination, forecast, and prevention urge enterprises to employ workers under the law, effectively reduce labor disputes, avoid the rise of conflicts, and maintain the stability of enterprises and society. In the background of building a harmonious society, the employment of enterprises has been paid more attention by the government, industry, and schools. Particularly for enterprises, market competition largely comes down to talent competition. In the global talent shortage, legal employment and risk prevention have been placed in front of enterprises. This is especially true for state-owned enterprises. More, under the prevailing situation of protecting workers’ rights, interests, and increasing legal awareness of enterprises and workers, it is more prominent for state-owned enterprises to carry the mission and responsibility of building a harmonious society. Its employment plays a leading role in the whole society or industry. The pragmatic purpose of this work is to deal with the risk management-related theory to the study of legal risk prevention and control of employment using the method of risk identification related to the current domestic and foreign through decision tree algorithm (DTA) about the ocean enterprise employee to assess international agreements and legal rules. Given the ocean enterprise employee specifically the seafarers’ employment combined with industry norms, the ideas, and measures of legitimate employment, this study has a strong theoretical and practical guiding importance for practical work.


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## Body

## 1. Introduction

Based on self-examination and evaluation of enterprises, labor law volunteers are organized to go deep into enterprises for “consultation” and “inspection” to assess employment risks and urge enterprises to regulate employment in view of problems, effectively reducing the occurrence of labor disputes. Although the exploration has achieved acceptable results, on the whole, at present, the risks of enterprise labor and employment are still prominent, and there are still some problems and deficiencies in many places including:(1)
The responsibility of risk assessment is not clear, and the team is not sound. It is unclear whether this mechanism should be implemented in the human resource (HR) department or the labor union. The risk forecaster team among the front-line workers is not sound, and the ideological dynamics of the workers are not mastered(2)
The risk assessment index system is not perfect. The index system closely related to the stability of the workforce, such as the statistics of laid-off workers, unemployment rate, the proportion of difficult jobs, and other indicators, is not perfect(3)
The channels for reporting worrying information, especially emergency information, are not smooth. The contradiction between the hierarchical reporting of information and the hierarchical management of events and the target responsibility system of “who is in charge and who is responsible” is quite prominent. The phenomenon of “unreported worries” in information reporting is more serious(4)
System implementation is not in place. Once risk points are identified, corrective actions are difficult to put in placeGenerally speaking, there are risk points in enterprise employment, resulting in damage to workers’ rights and interests, labor disputes, and emergencies; there are germination brew and outbreak of three stages. The emphasis and main attention of the risk assessment and prediction mechanism should be placed on the first stage, and efforts should be made to control the second stage and prevent the contradiction from developing to the third stage as far as possible. Enterprises should strengthen internal self-coordination and establish a sound mechanism for the source prevention of labor disputes. We will strengthen publicity and enforcement of laws and regulations such as the Labor Law and Regulations on Letters and Visits. We should strengthen legal education among business operators and employees, popularize legal knowledge, and enhance legal awareness by the law and discipline ourselves according to law. We will give full play to the democratic management system of enterprise workers and the mechanism for coordinating labor-management relations. The enterprise must establish and improve the generation and the trade union organization. We will observe to and improve the collective contracts equal consultation, enterprise business report to “ZhiDaiHui” expenses, “ZhiDaiHui” democratic assessment enterprise leading cadres system, such as a factory, expanding links with worker masses, open channels of dialogue, and improve worker masses master of masters Weng consciousness.We will do a good job in ideological and political work among workers and the masses. Listen to the call, the reasonable requirements of the masses to solve, not immediately solutions to explain, do a good job, explain unable to solve or to unreasonable, be patient to do a good job of persuasion to influence, can never avoid contradictions, perfunctory, cannot take more simple and crude attitude, methods suppressing crowds, and create conflict. We will strengthen and improve ideological and political work, teach workers to correctly understand and deal with difficulties and problems arising in the reform process, enhance their risk and psychological tolerance, and guide workers to act by the law on their own and to respond to and solve problems by normal channels, legal channels, and procedures. We will strengthen research on social early-warning indicators and establish a dynamic monitoring mechanism for labor relations.The establishment of monitoring mechanism is the “detector” to maintain social stability. Establish a completely suitable for the situation of our country’s social early warning index system of the former, statistics, letters and visits, trade unions, labor, and other departments can put a few especially close to protecting social stability index, such as rising prices index, the urban residents living standard as its core index for advanced research, analysis, regularly to laid-off and unemployed workers at the same time. The living condition of the difficult worker, especially poor workers, is “three security lines,” and the party and government have policies and regulations of close worker’s life to carry out the situation to undertake analysis and forecast. Strengthen the research on the ideological status of workers. Government concerned branch and trade union organization should make use of a scientific method, undertake rapid, accurate public opinion survey in workers to a certain number of social phenomenon and hot spot problems, and reflect worker’s view to political, economic phenomenon, and social atmosphere in time. Before the rapid growth of unstable factors, timely measures should be taken to eliminate them in the bud. This work aims the following points to be targeted.(i)
The main purpose of this study is to deal with the risk management-related theory to the study of legal risk avoidance and control of employment(ii)
This study used the analytic hierarchy process (AHP) to split information security risk valuation into four key factors and further subdivide it into 21 categories of indicators(iii)
This study used risk identification techniques related to the current native and foreign through a novel Unbiased Iterative Brunching (UIB) decision tree algorithm(iv)
The UIB decision tree algorithm takes into account the selection bias problem and the unpredictable attribute missing data in the traditional decision tree algorithm(v)
This study used ocean enterprise employees to assess international agreements and legal rules, specifically, the seafarers’ employment combined with industry norms, the ideas, and measures of legitimate employment(vi)
This study has a strong theoretical and practical guiding importance for practical work, and finally, the advantages and disadvantages of UIB are analyzed


## 2. Literature Review

The supporting role of information in the business application becomes more obvious; information security risk assessment has been attached to great importance to government agencies, financial telecommunications, and energy; and other large enterprises has become the basic work of information security [1]. How to improve the effectiveness of information system risk assessment and how improve the accuracy of business application system security assessment are the key issues to be considered at present. Qualitative evaluation methods mainly rely on the knowledge and experience of experts, which are subjective and require the high quality of the evaluators themselves. However, quantitative assessment methods use mathematical and statistical tools to describe risks, which are easy to simplify and blur the complex system, and some risk parameters may be lost or misinterpreted after quantification. Therefore, comprehensive assessment methods combining qualitative and quantitative methods are commonly used. Analytic Hierarchy Process is a multiobjective decision analysis method that combines both qualitative and quantitative methods [2]. This method performs hierarchical, quantitative, and standardized processing on the system, and its advantage is to decompose complex problems into multiple simple problems in a hierarchical structure.At present, some risk assessment models and methods based on the ANALYTIC hierarchy process have been produced [3–5]. Santos et al. [4] and Zhihui et al. [5] proposed a method based on support vector machine (SVM) [6]. This method has a low principal view, strong adaptability to the test environment of small sample data, high classification accuracy, and can effectively prevent overlearning [5]. However, the SVM solution is an optimization process, requiring a lot of calculation. In addition, it also requires uniform distribution of data categories, which is not easy to meet in information security risk assessment. The decision tree classification algorithm based on C4.5 is based on the information gain rate to build a decision tree. In the training stage and test stage, only a simple comparison is required, so the calculation is simple, and there is no requirement for data categories.Information security risk assessment is to determine the expected loss amount of the entire system caused by the loss or destruction of each resource in the computer system and network and to assess the threat, weakness, and the resulting risk [1]. Therefore, it is necessary to choose the appropriate risk evaluation method according to the specific situation of the system [4]. With reference to the national standard of information security risk assessment [7] and combined with the actual situation of domestic industry risk assessment projects, this paper uses AHP to divide information security risk assessment into four key factors including asset valuation, threat analysis, and analysis of existing measures shown in Figure 1, and further, it subdivides it into 21 categories of indicators, which makes the hierarchical structure.Figure 1
Elements of information security risk assessment.


## 3. Current Situation, Objectives, and Principles of Legal Risk Prevention and Control of State-Owned Enterprises

### 3.1. Basic Status

The legal factors leading to legal risks of enterprises mainly include a certain legal state or the behavior of legal subjects, including legal environment factors and enterprises’ factors E311. It can be seen from the analysis of the preceding chapter that the employment of state-owned ocean shipping enterprises is the same. We should establish a social early warning index system fully suitable for China’s national conditions, including statistics, letters and visits, trade unions, labor, and other departments. We take several indicators that are particularly close to ensuring social stability, such as the price rise index and the living standards of urban residents, as its core indicators for further research and analysis. Whether it is large central enterprises such as COSCO and China Shipping Group or COSCO companies with local state-owned assets such as Hunan COSCO and Jiangxi COSCO, the weak links in risk prevention and control come from the two aspects of law and the enterprise itself. Although the group has made many positive efforts, such as COSCO Group’s timely revision of COSCO crew management measures and COSCO Group's introduction of China Maritime crew management responsibility definition, there are many deficiencies in general, such as weak centralized embodiment ability, weak legal risk prevention awareness of some employees, imperfect enterprise project management system, weak implementation of risk prevention and control, nonstandard legal system, Insufficient investment in legal risk prevention and control, and lagging risk prevention mechanism.


### 3.2. Overall Objectives

By discovering, identifying, analyzing, controlling, supervising, and dealing with the domestic and foreign conventions and legal risks faced by the employees of state-owned ocean shipping enterprises [8], realize the optimization and balance of legal risks borne by enterprises and operating benefits; that is to say, enterprises should pursue the maximum benefit on the premise of the lowest legal risk or pursue the minimum legal risk on the premise of a certain income.


### 3.3. Main Principles

For the prevention and control of legal risks in the employment of state-owned ocean shipping enterprises, legal risk prevention and control systems must be established and perfected by the actual employment situation [9–11]. The enterprise legal risk prevention and control system must be complete, scientific, and effective at the same time, to truly serve the business development of enterprises and effectively play the role of escort. Therefore, state-owned long-distance transportation enterprises must adhere to the following main principles to establish legal risk prevention and control systems and improve prevention and control capacity: first, comprehensive coverage and full participation. Modern enterprise management is based on the foundation, which is not only the methodology of enterprise management but also the core position of practicing the scientific concept of development. The same is true in practice. The comprehensiveness of legal risk prevention and control in the employment of state-owned ocean shipping enterprises is reflected in every field, every participant, and every management method involved in the employment of land and seafarers, which runs through the whole process of enterprise operation and management, touching every link, department, and post. Risk prevention and control require the participation of all staff, including enterprise decision-makers, management, grass-roots employees, and legal advisers. Through effective measures, the goal of legal risk control is finally achieved.Second, prevention first and comprehensive treatment: the employment of state-owned ocean shipping enterprises may lead to serious consequences and irreparable losses, such as international influence and disputes, social stability and political and economic orientation, the overall image of the party and the country, and the loss of the enterprise itself. Therefore, prevention must be taken from the source, and the prevention should be given priority in advance, the handling of the matter in the middle, and the remedy afterward as a supplement. At the same time, we should also adhere to comprehensive governance [12]. On the one hand, we should give full play to the advantages of state-owned enterprises, especially the role of international and domestic mass organizations and enterprises’ own party labor league organizations, and actively mediate and resolve legal disputes as much as possible. On the other hand, the legal risk of employment has a strong correlation with other risks, such as financial risk and management risk, so the cooperation between departments should be strengthened, and the symptoms and root causes should be addressed simultaneously.Third, standardized operation and effective oversight: legal risk prevention and control of employment need to have a practical and feasible supervision process and system guarantee, such as risk information collection, evaluation, and preventive measures, contingency plans, etc., in particular, to clear the key departments and key positions in the responsibilities and role of risk management and labor law reasonable configuration enterprise labor resources and to establish and perfect the system of recruitment and risk management. The prevention and control of employment risks will be incorporated into enterprise management processes and systems to lay a solid foundation for legal risk prevention [13].Fourth, practical and dynamic adjustment: international conventions, regional agreements, domestic legal provisions, and the internal situation of enterprises will change with the development of the social-political, economic, and cultural environment, and the nature, types, forms, intensity, and scope of influence of legal risks of employment of state-owned ocean shipping enterprises may change. Therefore, enterprises must timely adjust the employment risk prevention and control scheme, evaluate the new legal risks, and take targeted preventive measures to achieve dynamic management [14, 15].


### 3.4. Application of Risk Management Theory and Employment Legal Risk Prevention and Control

In the theory of risk management, integrated risk management, IRM, and COSO (The Committee of Sponsoring Organizations) of The National Commission of Fraudulent Financial 3, the comprehensive risk management framework plays a realistic, systematic, and long-term guiding role in the legal risk prevention and control of state-owned ocean shipping enterprises. The main idea of integrated risk management is to maximize enterprise value or shareholder value as the goal, take the whole operation and management activities of the enterprise as the object of investigation, comprehensively analyze and consider all the risks that the enterprise may face now and in the future, and make full use of the nature that different risks can offset, influence, and correlate with each other. According to the logical process of risk identification, risk analysis, risk assessment, and risk control, with the help of a variety of computer technology and mathematical tools, timely and effectively control and eliminate the factors that have a negative impact on enterprise value, in order to dig and use the potential development and profit opportunities of the enterprise.


### 3.5. Main Measures for the Prevention and Control of Legal Risks in Employment of State-Owned Ocean Shipping Enterprises

According to the analysis and research of the preceding chapter, it can be known that the employment of state-owned ocean shipping enterprises should not only meet the law of political and economic development but also meet the standard requirements of domestic and foreign conventions, laws, and regulations and meet the special requirements of seafarers. In terms of legal risk prevention and control, it is necessary to have clear prevention and control objectives and principles and make full use of risk management theory to guide the prevention and control in an all-around way.


## 3.1. Basic Status

The legal factors leading to legal risks of enterprises mainly include a certain legal state or the behavior of legal subjects, including legal environment factors and enterprises’ factors E311. It can be seen from the analysis of the preceding chapter that the employment of state-owned ocean shipping enterprises is the same. We should establish a social early warning index system fully suitable for China’s national conditions, including statistics, letters and visits, trade unions, labor, and other departments. We take several indicators that are particularly close to ensuring social stability, such as the price rise index and the living standards of urban residents, as its core indicators for further research and analysis. Whether it is large central enterprises such as COSCO and China Shipping Group or COSCO companies with local state-owned assets such as Hunan COSCO and Jiangxi COSCO, the weak links in risk prevention and control come from the two aspects of law and the enterprise itself. Although the group has made many positive efforts, such as COSCO Group’s timely revision of COSCO crew management measures and COSCO Group's introduction of China Maritime crew management responsibility definition, there are many deficiencies in general, such as weak centralized embodiment ability, weak legal risk prevention awareness of some employees, imperfect enterprise project management system, weak implementation of risk prevention and control, nonstandard legal system, Insufficient investment in legal risk prevention and control, and lagging risk prevention mechanism.


## 3.2. Overall Objectives

By discovering, identifying, analyzing, controlling, supervising, and dealing with the domestic and foreign conventions and legal risks faced by the employees of state-owned ocean shipping enterprises [8], realize the optimization and balance of legal risks borne by enterprises and operating benefits; that is to say, enterprises should pursue the maximum benefit on the premise of the lowest legal risk or pursue the minimum legal risk on the premise of a certain income.


## 3.3. Main Principles

For the prevention and control of legal risks in the employment of state-owned ocean shipping enterprises, legal risk prevention and control systems must be established and perfected by the actual employment situation [9–11]. The enterprise legal risk prevention and control system must be complete, scientific, and effective at the same time, to truly serve the business development of enterprises and effectively play the role of escort. Therefore, state-owned long-distance transportation enterprises must adhere to the following main principles to establish legal risk prevention and control systems and improve prevention and control capacity: first, comprehensive coverage and full participation. Modern enterprise management is based on the foundation, which is not only the methodology of enterprise management but also the core position of practicing the scientific concept of development. The same is true in practice. The comprehensiveness of legal risk prevention and control in the employment of state-owned ocean shipping enterprises is reflected in every field, every participant, and every management method involved in the employment of land and seafarers, which runs through the whole process of enterprise operation and management, touching every link, department, and post. Risk prevention and control require the participation of all staff, including enterprise decision-makers, management, grass-roots employees, and legal advisers. Through effective measures, the goal of legal risk control is finally achieved.Second, prevention first and comprehensive treatment: the employment of state-owned ocean shipping enterprises may lead to serious consequences and irreparable losses, such as international influence and disputes, social stability and political and economic orientation, the overall image of the party and the country, and the loss of the enterprise itself. Therefore, prevention must be taken from the source, and the prevention should be given priority in advance, the handling of the matter in the middle, and the remedy afterward as a supplement. At the same time, we should also adhere to comprehensive governance [12]. On the one hand, we should give full play to the advantages of state-owned enterprises, especially the role of international and domestic mass organizations and enterprises’ own party labor league organizations, and actively mediate and resolve legal disputes as much as possible. On the other hand, the legal risk of employment has a strong correlation with other risks, such as financial risk and management risk, so the cooperation between departments should be strengthened, and the symptoms and root causes should be addressed simultaneously.Third, standardized operation and effective oversight: legal risk prevention and control of employment need to have a practical and feasible supervision process and system guarantee, such as risk information collection, evaluation, and preventive measures, contingency plans, etc., in particular, to clear the key departments and key positions in the responsibilities and role of risk management and labor law reasonable configuration enterprise labor resources and to establish and perfect the system of recruitment and risk management. The prevention and control of employment risks will be incorporated into enterprise management processes and systems to lay a solid foundation for legal risk prevention [13].Fourth, practical and dynamic adjustment: international conventions, regional agreements, domestic legal provisions, and the internal situation of enterprises will change with the development of the social-political, economic, and cultural environment, and the nature, types, forms, intensity, and scope of influence of legal risks of employment of state-owned ocean shipping enterprises may change. Therefore, enterprises must timely adjust the employment risk prevention and control scheme, evaluate the new legal risks, and take targeted preventive measures to achieve dynamic management [14, 15].


## 3.4. Application of Risk Management Theory and Employment Legal Risk Prevention and Control

In the theory of risk management, integrated risk management, IRM, and COSO (The Committee of Sponsoring Organizations) of The National Commission of Fraudulent Financial 3, the comprehensive risk management framework plays a realistic, systematic, and long-term guiding role in the legal risk prevention and control of state-owned ocean shipping enterprises. The main idea of integrated risk management is to maximize enterprise value or shareholder value as the goal, take the whole operation and management activities of the enterprise as the object of investigation, comprehensively analyze and consider all the risks that the enterprise may face now and in the future, and make full use of the nature that different risks can offset, influence, and correlate with each other. According to the logical process of risk identification, risk analysis, risk assessment, and risk control, with the help of a variety of computer technology and mathematical tools, timely and effectively control and eliminate the factors that have a negative impact on enterprise value, in order to dig and use the potential development and profit opportunities of the enterprise.


## 3.5. Main Measures for the Prevention and Control of Legal Risks in Employment of State-Owned Ocean Shipping Enterprises

According to the analysis and research of the preceding chapter, it can be known that the employment of state-owned ocean shipping enterprises should not only meet the law of political and economic development but also meet the standard requirements of domestic and foreign conventions, laws, and regulations and meet the special requirements of seafarers. In terms of legal risk prevention and control, it is necessary to have clear prevention and control objectives and principles and make full use of risk management theory to guide the prevention and control in an all-around way.


## 4. Decision Tree Algorithm

This paper refers to the classification method based on the C4.5 decision tree [8] for information security risk assessment. The main idea of the C4.5 algorithm is to gradually find out the variable attributes of each level that provide the maximum information gain rate. The information gain rate is used to select variable attributes, and the entropy of the training sample is calculated first, which can be expressed by using
(1)infoT=−∑j=1kfreqCj,S∣S∣•log2freqCj,S∣S∣.Among them,freqCj,S in the set S belongs to k may be one of the Ci number of samples in class and set the number of samples in S1S∣. Divide T into sets T1,⋯,Tj, these subsets need to be weighted by entropy, and the calculation is done by using
(2)infoT=−∑i=1jTiT•infoTi.The difference before and after discrimination is calculated to obtain the information gain, whose calculation is represented by(3)GainX=infoT−infoxT.To make up for this deficiency, the information gain rate is defined, which takes into account the number and size of the child nodes generated by each partition and divides the objects to be considered one by one, regardless of the information contained in the classification, which is represented by(4)Split_InfoxX=−∑i=1sTiT•logTiT.The information gain rate of attributeX is defined by suing
(5)Gain_rationX=GainXSplit_InfoxX.


### 4.1. Data Classification

The number of samples in the database to be classified is known, and the classification is based on the attributes of the data. By analyzing and classifying different attribute combinations and corresponding categories of training sample data, rules hidden in attribute and category can be found, and data of unknown categories can be classified. According to the different classification functions and classification model, the data may be classified into different categories. To construct a classification model, a series of training sample data sets are required as inputs. We can also secrete the training sample data set as a training set, which consists of a series of records. Each record contains several attributes of the data, as well as the specific class tag to which the data belongs. Class tags, along with historical data, are input parameters to the system. A concrete sample of data can be represented by anA vector: (A,A,.,A,;C). Here, A indicates the data attribute, and C indicates the classification to which the data belongs.The purpose of classification is to analyze the data in the training sample set and find an accurate model or description for each class by analyzing the relationship between attributes and categories. The resulting model classifies future unknown categories of data. The following are three steps of data classification: first, according to the given training sample set, find the mapping functionH:Fx−c between attributes and categories. This stage is called the training stage of the model; second, the function model obtained by training is tested with test data and the model is adjusted. Third, the data model is used to predict the categories of unknown data.The decision tree algorithm is one of the commonly used inductive classification algorithms. Its goal is to derive classification rules from irregular instances, establish classification models, and express the rules in a tree structure graph. Compared with neural network and Bayesian methods, a decision tree does not need a lot of time to conduct thousands of iterative training models, so it is suitable for large-scale data sample sets. In addition to the information presented by the data in the training sample, the decision tree method does not need other additional information to perform good classification accuracy. The decision tree consists of three parts: nonleaf node, branch, and leaf node. A nonleaf node represents a test of an attribute of an instance, each subsequent branch of the node corresponds to a judgment of a possible value of an attribute, and a leaf node represents a category to which the instance belongs.The generation of decision numbers is a process of dividing and ruling from top to bottom. The decision tree starts from the root node and tests the sample data. According to different results, the data samples are divided into different data sample subsets, and each data sample subset constitutes a subnode. Then, each subnode is divided recursively until the data in each subset of the data sample belongs to the same category. For the generated decision tree, to classify a certain data, test the specified attributes of the current node starting from the root node of the tree, select the correct branch, and move down to the leaf node of the tree according to the different attribute values. The category corresponding to the leaf node is the category to which the sample should belong. Therefore, data classification is also a top-down process.Decision tree technology applies a depth-first greedy search strategy, which tests each attribute value in turn. First, an attribute of the task is tested. After classification, a recursive attribute test is performed on the recursion of the sample subset to which an attribute value belongs. A branch of the decision tree keeps increasing until the information gained obtained by a certain attribute test does not increase any more, then it falls back to another book subset obtained by the previous attribute test. The characteristic of greedy algorithms is that they always choose the current best choice rather than consider the best overall. The learning algorithm based on a decision tree can generate high precision and understandable rules at a fast speed, but the computation is relatively small. In addition, a decision tree algorithm can deal with continuous value and discrete value attributes, and users do not need to know a lot of background knowledge in the learning process. As long as the training sample data can be formally expressed by attribute conclusions, the algorithm can be used to learn. There are three kinds of ideal decision trees: (1) the least number of leaves; (2) the depth of leaf node is minimum; (3) the minimum number of leaf nodes and the minimum depth of leaf nodes.However, Hong Jiarong et al. have proved that finding such an optimal decision tree is an NP problem. Therefore, the purpose of decision tree optimization is to find the optimal decision tree as far as possible. The specific generation algorithms of the decision tree mainly include ID3, C4.5, CART (Classification and Regression Tree), and CHAID (Chi-Square Automatic Interaction Detector, Chi-square white Interaction Detector) methods. In inductive learning, it represents a large category based on the decision tree method. Where ID3 and later C4.5 are both Quinlan’s concept learning system in hunt, however, the C4.5 algorithm is also an improved algorithm proposed by Quinlan himself against 1D3 algorithm, and he described the C4.5 algorithm in detail in his monograph Machine Learning Planning & GT; & GT. The CHAID algorithm was proposed by Dr. Gordon B. Kass in 1980, which can be used to mine classified data. The CART algorithm has been popularized since 1984, which can process continuous dependent variables.Aimed at the disadvantage of these algorithms, many researchers tried to control the size of the tree and simplify the aspects of the decision tree, by studying the various pre and after pruning algorithms to control the size of the tree, modify the test attribute space at the same time, improve test attribute selection methods and limited data set, change the data structure, and so on, many of the new algorithms were presented and standards. The transmitted information in the source includes mutually exclusive and combined complete events of a limited number of days. The occurrence of each event is subject to a certain probability, which is formally expressed as a group of eventsX1,X2⋯, the occurrence probability of Xr is px1.Px2,⋯Pxr. This information entropy HX is equal to the mathematical expectation of information amount IX of each event that is represented by
(6)HX=−∑i=1rpXiIXi=−∑i=1rpXilogpXi.LetT be the set of data samples containing m sample data. Suppose the data sample contains different categories in KC1,C2⋯,ck. Let ci be the number of samples in the subset Ci. For a given sample classification, its information entropy is calculated by using
(7)Ic1,c2,⋯,ck=−∑i=1kpilogpi.According to the information entropy divided into subsets byA, it can be represented by
(8)EAi=∑i=1rtimItci1,tci2,⋯,tcik.The termti/m is the weight of the first category subset and is equal to the number of samples in the subset divided by the total number of samples. The lower the information entropy of partition, the higher the purity of subset partition. In Equation (8), Itci1,tci2,⋯,tcik represents the information entropy of sample subset C obtained after partitioning according to attribute 4 using
(9)Itci1,tci2,⋯,tcik=−∑i=1kpijlogpij,where Pij represents the probability of belonging to category Cj in subset Ti, denoted as P=C. The information gain obtained by selecting attribute A is represented by
(10)GainAi=Ic1,c2,⋯,ck−EAi.


## 4.1. Data Classification

The number of samples in the database to be classified is known, and the classification is based on the attributes of the data. By analyzing and classifying different attribute combinations and corresponding categories of training sample data, rules hidden in attribute and category can be found, and data of unknown categories can be classified. According to the different classification functions and classification model, the data may be classified into different categories. To construct a classification model, a series of training sample data sets are required as inputs. We can also secrete the training sample data set as a training set, which consists of a series of records. Each record contains several attributes of the data, as well as the specific class tag to which the data belongs. Class tags, along with historical data, are input parameters to the system. A concrete sample of data can be represented by anA vector: (A,A,.,A,;C). Here, A indicates the data attribute, and C indicates the classification to which the data belongs.The purpose of classification is to analyze the data in the training sample set and find an accurate model or description for each class by analyzing the relationship between attributes and categories. The resulting model classifies future unknown categories of data. The following are three steps of data classification: first, according to the given training sample set, find the mapping functionH:Fx−c between attributes and categories. This stage is called the training stage of the model; second, the function model obtained by training is tested with test data and the model is adjusted. Third, the data model is used to predict the categories of unknown data.The decision tree algorithm is one of the commonly used inductive classification algorithms. Its goal is to derive classification rules from irregular instances, establish classification models, and express the rules in a tree structure graph. Compared with neural network and Bayesian methods, a decision tree does not need a lot of time to conduct thousands of iterative training models, so it is suitable for large-scale data sample sets. In addition to the information presented by the data in the training sample, the decision tree method does not need other additional information to perform good classification accuracy. The decision tree consists of three parts: nonleaf node, branch, and leaf node. A nonleaf node represents a test of an attribute of an instance, each subsequent branch of the node corresponds to a judgment of a possible value of an attribute, and a leaf node represents a category to which the instance belongs.The generation of decision numbers is a process of dividing and ruling from top to bottom. The decision tree starts from the root node and tests the sample data. According to different results, the data samples are divided into different data sample subsets, and each data sample subset constitutes a subnode. Then, each subnode is divided recursively until the data in each subset of the data sample belongs to the same category. For the generated decision tree, to classify a certain data, test the specified attributes of the current node starting from the root node of the tree, select the correct branch, and move down to the leaf node of the tree according to the different attribute values. The category corresponding to the leaf node is the category to which the sample should belong. Therefore, data classification is also a top-down process.Decision tree technology applies a depth-first greedy search strategy, which tests each attribute value in turn. First, an attribute of the task is tested. After classification, a recursive attribute test is performed on the recursion of the sample subset to which an attribute value belongs. A branch of the decision tree keeps increasing until the information gained obtained by a certain attribute test does not increase any more, then it falls back to another book subset obtained by the previous attribute test. The characteristic of greedy algorithms is that they always choose the current best choice rather than consider the best overall. The learning algorithm based on a decision tree can generate high precision and understandable rules at a fast speed, but the computation is relatively small. In addition, a decision tree algorithm can deal with continuous value and discrete value attributes, and users do not need to know a lot of background knowledge in the learning process. As long as the training sample data can be formally expressed by attribute conclusions, the algorithm can be used to learn. There are three kinds of ideal decision trees: (1) the least number of leaves; (2) the depth of leaf node is minimum; (3) the minimum number of leaf nodes and the minimum depth of leaf nodes.However, Hong Jiarong et al. have proved that finding such an optimal decision tree is an NP problem. Therefore, the purpose of decision tree optimization is to find the optimal decision tree as far as possible. The specific generation algorithms of the decision tree mainly include ID3, C4.5, CART (Classification and Regression Tree), and CHAID (Chi-Square Automatic Interaction Detector, Chi-square white Interaction Detector) methods. In inductive learning, it represents a large category based on the decision tree method. Where ID3 and later C4.5 are both Quinlan’s concept learning system in hunt, however, the C4.5 algorithm is also an improved algorithm proposed by Quinlan himself against 1D3 algorithm, and he described the C4.5 algorithm in detail in his monograph Machine Learning Planning & GT; & GT. The CHAID algorithm was proposed by Dr. Gordon B. Kass in 1980, which can be used to mine classified data. The CART algorithm has been popularized since 1984, which can process continuous dependent variables.Aimed at the disadvantage of these algorithms, many researchers tried to control the size of the tree and simplify the aspects of the decision tree, by studying the various pre and after pruning algorithms to control the size of the tree, modify the test attribute space at the same time, improve test attribute selection methods and limited data set, change the data structure, and so on, many of the new algorithms were presented and standards. The transmitted information in the source includes mutually exclusive and combined complete events of a limited number of days. The occurrence of each event is subject to a certain probability, which is formally expressed as a group of eventsX1,X2⋯, the occurrence probability of Xr is px1.Px2,⋯Pxr. This information entropy HX is equal to the mathematical expectation of information amount IX of each event that is represented by
(6)HX=−∑i=1rpXiIXi=−∑i=1rpXilogpXi.LetT be the set of data samples containing m sample data. Suppose the data sample contains different categories in KC1,C2⋯,ck. Let ci be the number of samples in the subset Ci. For a given sample classification, its information entropy is calculated by using
(7)Ic1,c2,⋯,ck=−∑i=1kpilogpi.According to the information entropy divided into subsets byA, it can be represented by
(8)EAi=∑i=1rtimItci1,tci2,⋯,tcik.The termti/m is the weight of the first category subset and is equal to the number of samples in the subset divided by the total number of samples. The lower the information entropy of partition, the higher the purity of subset partition. In Equation (8), Itci1,tci2,⋯,tcik represents the information entropy of sample subset C obtained after partitioning according to attribute 4 using
(9)Itci1,tci2,⋯,tcik=−∑i=1kpijlogpij,where Pij represents the probability of belonging to category Cj in subset Ti, denoted as P=C. The information gain obtained by selecting attribute A is represented by
(10)GainAi=Ic1,c2,⋯,ck−EAi.


## 5. Experimental Analysis

In this chapter, we test the proposed money laundering risk assessment model by programming the UIB algorithm. Since the purpose of this paper is to evaluate customer money laundering risk through testing data attributes by improving the algorithm, the accuracy of customer money laundering risk assessment is taken as the performance indicator of this model in this experiment. We train the training set to produce a set of risk assessment rules. After inputting the above training data set into the program, we get the rule set as follows. The rule set after running the output is shown in Figure2.Figure 2
The rule set after running the output.By outputting the rule set above, we can get the decision tree graph as shown in Figure2. This decision tree can intuitively describe the relationship between each noncategory attribute and money laundering risk level. After the decision tree algorithm, 15 test cases in the test set were used to prune the abnormal branches in the generated decision tree, and the output rule set as shown in Figure 3 was obtained.Figure 3
Pruned rule set.After the program is run and all the data pass the judgment of the rule set, the output risk assessment performance result is shown in Figure4.Figure 4
Employment law evaluation results.We set the number of training set samples to 300, 1000, and 5000, respectively, to test the performance changes of the UIB algorithm with the increase of training set samples. The number of samples in the validation set is 300, among which the number of missing attributes accounts for 10%. For each group of experiments, we repeated 10 times and obtained the final evaluation accuracy performance parameters by averaging the results of 10 times. Table1 shows the performance comparison results of the ID3 algorithm and improved UIB algorithm.Table 1
Performance comparison results of ID3 algorithm and improved UIB algorithm.
Number of training set samplesSample size of validation setEvaluation accuracyNumber of complete samplesNumber of missing samplesID3UIB3002703075.43%79.87%10002703076.37%81.53%50002703070.70%77.46%By comparing the accuracy of the validation set with the ID3 decision tree algorithm and UIB decision tree algorithm, we know that, on the whole, the accuracy of predicting unknown customer risk levels by decision rules generated by the UIB algorithm is much higher than that by decision rules generated by 1D3 algorithm. This is because 30 of the 300 validation data are missing noncategorical attributes, so their attributes cannot be predicted by the traditional ID3 decision tree algorithm. UIB algorithm is proposed, and we produce rule sets to verify the concentration data to forecast; as a result, the algorithm rule set to record the probability of each attribute value, when to meet the classification properties of missing cases, using the method of rotation of missing attribute value estimate, which could classify the risk level of this kind of data. Experimental results show that the evaluation accuracy of the UIB algorithm is up to 6.8% higher than that of the 1D3 algorithm. Therefore, the prediction accuracy of this algorithm is greatly improved compared with the accuracy of the traditional 1D3 algorithm.According to previous experience, the more samples in the training set, the more mature the rule set will be and the more accurate the risk assessment for customers will be. According to our evaluation results, we can see that when the training sample set increases from 300 to 1000, the evaluation accuracy of ID3 and UIB algorithms increases accordingly. However, when the data in the training sample set increased to 5000, the evaluation accuracy was worse than that of the first two groups of experiments. The reasons are analyzed as follows: as a result, we discrete the risk value of each attribute into three levels of “low,” “medium,” and “high,” and the value range of the three levels is (0, 0.3], (0.3, 0.6], and (06, 1), respectively, resulting in a large deviation of the final classification attribute. In order to verify the correctness of this interpretation, we divided the risk level into five levels (low, low, medium, high, and high), and the corresponding risk ranges are (0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.81, and (0.8, 11). The evaluation accuracy of the two algorithms is shown in Table2.Table 2
The evaluation accuracy of the two algorithms.
Number of training set samplesRank numberEvaluation accuracyID3UIB5000370.70%77.46%577.77%84.60%According to the assessment accuracy in Table2, the assessment results are more accurate when the risk level is divided into five levels than when the risk level is divided into three levels. This is because the more risk levels there are, the more refined the rule set and the more rules there are in the rule set. Therefore, the more accurate the risk level assessment is.


## 6. Conclusion

State-owned ocean shipping enterprise labor legal risk, especially the seafarers’ employment risk, there are both international and domestic, both economic and political aspects, and both general and special, while thousands of Venice, and intricate. However, with careful study and positive response, we will be able to effectively avoid legal risk, construct harmonious enterprise, and accelerate the construction process of a socialist harmonious society. In the prevention and control of labor risk, the state, enterprises, and employees need to make joint efforts to form a unified system. The important step in data mining is data classification. In this paper, a new UIB decision tree algorithm is proposed. This algorithm takes into account the selection bias problem and the unpredictable attribute missing data in the traditional decision tree algorithm. Finally, the advantages and disadvantages of the UIB are analyzed. Experimental results show that the evaluation accuracy of UIB algorithm is up to 6.8% higher than that of the 1D3 algorithm. Hence, the prediction accuracy of this algorithm is significantly enhanced compared with the accuracy of the traditional 1D3 algorithm.



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*Source: 6705542-2022-04-14.xml*