Improved Decision Tree Algorithm Research Articles

An improved decision tree algorithm based on hierarchical neighborhood dependence

An improved decision tree algorithm based on hierarchical neighborhood dependence

Classification method of English micro-course resources on MOOC platform based on improved decision tree algorithm

Classification method of English micro-course resources on MOOC platform based on improved decision tree algorithm

An improved decision tree algorithm based on boundary mixed attribute dependency

An improved decision tree algorithm based on boundary mixed attribute dependency

Evaluation Model of Modern Network Teaching Quality Based on Artificial Intelligence E-Learning

Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing principles of improved decision tree algorithm, and the information gain of the algorithm determines the inheritance of information. The process of modern e-learning system is based on personalized teaching and humanized intelligent interaction. This paper theoretically analyzes the improvement performance of the existing e-learning system in teaching quality evaluation and shows a good classification effect. This model provides reference materials for the expansion of education and teaching and provides a feasible practical model for personalized teaching in online schools. The authors provide good educational conditions and environment for students and cultivate all-around talents for the society.

The Promotion of Women's Leisure Sports Behavior Based on Improved Decision Tree Algorithm

In women's daily leisure choices, sports is an important content that cannot be ignored. In this context, this paper studies the promotion of women's leisure sports behavior based on improved decision tree algorithm. Based on the simple analysis of the research progress of leisure sports and decision tree algorithm, a female leisure sports behavior model based on decision tree is constructed. Based on the decision tree algorithm, the calculation method of information gain rate is optimized to avoid logarithmic operation, and the continuous attributes are discretized. Simulation results show that in terms of classification accuracy, the improved decision tree algorithm is significantly higher than the classical decision tree algorithm, and can significantly shorten the running time, which has high application value in the realization of accurate classification analysis of female leisure sports behavior.

The Application of Data Mining Techniques in Employee Performance Assessment

Employee performance assessment is a powerful standard for measuring talent, and many companies pay more attention to the assessment of employee performance. Currently, there are many kinds of methods for employee performance evaluation. This leads to deficiencies in the data accuracy and data mining of current performance research. Therefore, to enhance the deep-level mining of performance data, the advantages of using methods are emphasized. This research uses data mining technology to measure employee performance and builds an improved ID3 decision tree algorithm model based on data mining technology, which can measure deeper employee performance. The experimental results show that the algorithm model is able to measure employee performance well, the accuracy of the decision tree algorithm is 93.2%, and the accuracy of the improved algorithm is 95.3%, so the improved algorithm is 39 ms shorter than the traditional algorithm in building the decision tree, and the algorithm accuracy is 2.1% higher. This shows that the improved decision tree algorithm of data mining technology can improve the precision and accuracy of employee performance evaluation.

Glass Composition Analysis and Identification based on Improved Decision Tree Algorithm

Glass is a valuable material evidence of our early trade exchanges, and our glass is mainly divided into high potassium glass and lead-barium glass. The specific types can be distinguished by examining the content of their internal compounds. This paper delineates the broad types of each unknown artefact and then predicts the composition before weathering. Combining the results of existing predicted subclasses as known data, 80% of the data is selected as the training set and 20% as the test set. Suitable node splitting evaluation criteria and feature division point selection criteria were determined, and a glass component analysis and identification model based on an improved decision tree algorithm was established to predict the major unknown artefact types separately and obtain their specific subclasses. The sensitivity analysis of the established identification model is carried out by adjusting the proportion of training data as well as the principal component weights.

A real-time data acquisition method of industrial production line based on OPC technology

In view of the low timeliness and accuracy of traditional production line data acquisition methods, this study proposed a real-time data acquisition method of industrial production line based on OPC technology. Considering the heterogeneity of industrial production line data sources, a data transmission scheme is generated based on OPC technology and multi-point communication with Modbus protocol after data preprocessing by piecewise linear interpolation method. Then, the improved decision tree algorithm is used to design the data processing mode. Finally, the current state of industrial production line data is judged by comparing the threshold value, and the real-time data collection of industrial production line is completed after the collection interval is determined. Experimental results show that the acquisition accuracy of this method is up to 96.8%, the maximum acquisition time is only 12.5 s, and the highest packet loss rate is only 1.89%, indicating that this method improves the acquisition quality.

Real-time recommendation method of online education resources based on improved decision tree algorithm

Real-time recommendation method of online education resources based on improved decision tree algorithm

An Improved Decision Tree Algorithm for Text classification and visualization

An Improved Decision Tree Algorithm for Text classification and visualization

An improved decision tree algorithm based on variable precision neighborhood similarity

The decision tree algorithm has been widely used in data mining and machine learning due to its high accuracy, low computational cost and high interpretability. However, when dealing with the continuous data, the classical decision tree algorithm needs to replace continuous attributes with discretized attributes by the strategy of discretization. Discretization may cause a loss of information structure, which will affect the performance of classification. To tackle this problem, many researchers have proposed different decision tree methods based on variable precision neighborhood rough sets. However, these methods do not consider the geometric structure of neighborhood systems, which may lead to a contradiction in the transitivity of the equivalence relation. In this paper, we first define a novel neighborhood geometric similarity in a neighborhood system from the perspective of geometry. Second, by combining the neighborhood geometric similarity and the neighborhood algebraic similarity, we propose four new kinds of neighborhood similarities, which can solve the contradictory transitivity of the equivalence relation. Third, a variable precision neighborhood rough set model is constructed using the new similarities, and a novel decision tree algorithm is proposed based on this model, where the degree of attribute dependence is used as the partition measure. Experimental results on 14 selected datasets from the UCI Machine Learning Repository show that our algorithm is effective. The average accuracy of our algorithm is over 90%, which is 10% higher than the classical decision tree algorithms, and the number of leaf nodes increases slightly.

Improvement Design of Functional Modules of College English Network Teaching System Based on Decision Tree Algorithm

English courses are a compulsory course for non-English core courses. Studying English at university is an important part of studying at a non-English university. The purpose of a university education in English is to enhance the comprehensive language skills of students in English. In this way, they can communicate effectively in English during future work, study, and social interactions, while strengthening their ability to meet the needs of social development and international exchange. The functional improvement design of the college English network teaching system will help the system to provide technical support and services for college English teaching more effectively. It can more effectively cultivate students’ language communicative ability and autonomous learning ability. This article first improves the practical units of the education system and then adds the decision tree algorithm. It uses improved decision-making algorithms to evaluate student performance with an improved learning system. The number of leaf nodes of the improved decision tree algorithm on the three datasets is 19, 14, and 15, respectively, and the number of summary points is 27, 23, and 7, respectively.

Design of Informatization College and University Teaching Management System Based on Improved Decision Tree Algorithm

At present, the teaching management system used in colleges cannot classify and store the teaching material information well and also has some problems, such as inaccurate calculation results of resource information weight, long response time, and large data query error. Therefore, this study designs an information college teaching management system based on improved decision tree algorithm. The hardware structure of the system consists of information communication structure, information teaching resource sharing structure, processor, and crystal oscillator circuit, and the core module is the data output control module. This study designs the system software based on the improved decision tree algorithm, creates a decision tree recursively, uses the CART decision tree to calculate the weight of teaching resource information, and constructs the fitness objective function of teaching resource information according to the mean clustering algorithm, so as to accurately extract the teaching resource information and realize the efficient processing of college teaching resource. The experimental results show that the response time of the system in this study is only 8 ms, the maximum convergence value is only 40, there is only one wrong data in the data query, and the storage time is 40 s. This system has a short response time, fast convergent rate, and a low probability of data search error when more than one client access the database at the same time.

Real time recommendation method of online education resources based on improved decision tree algorithm

Real time recommendation method of online education resources based on improved decision tree algorithm

Research on the Application of Improved Decision Tree Algorithm based on Information Entropy in the Financial Management of Colleges and Universities

In the era of information technology, the work relies on information technology to generate a huge amount of data and information. Among them, the financial data information of universities is growing exponentially, and the manual method of organizing data and extracting key information can no longer meet the requirements of financial data management of universities. Taking the financial management of higher education institutions as an example, it is difficult to grasp the progress of financial budget execution with frequent and complicated daily expenditure and income problems, and then it is difficult to execute correct decisions in the management. The study uses information entropy as the decision basis of decision tree in the financial management of higher education institutions. The higher the value of information entropy generated in financial management, the higher the prediction accuracy of the decision tree. The metric calculation method is introduced to obtain the information entropy as well as the information gain rate to predict the likelihood of problematic events. The study validates the performance of the improved decision tree with a dataset that achieves a maximum accuracy of 95% in the experiment. With the higher prediction accuracy, for the university financial management system, a decision tree for financial warning is established and the link between the current month's financial expenditure and the warning mechanism is analyzed, and finally the two common decision tree algorithms, (Iterative Dichotomiser3，ID3) ID3 and (Classification and regression tree，CART) CART, are compared with the algorithm proposed in the study. The mean square error and the sum of squared error metrics are used to conclude that the algorithm proposed in the study has better performance. By improving the existing decision tree algorithm, the study proposes a decision tree model based on information entropy, which aims to help decision makers to quickly and accurately distill relevant data and make correct decisions in a large amount of information data for more rational financial management.

Research on Students' Mental Health Based on Data Mining Algorithms.

With the diversification and rapid development of society, people's living conditions, learning and friendship conditions, and employment conditions are facing increasing pressure, which greatly challenges people's psychological endurance. Therefore, strengthening the mental health education of students has become an urgent need of society and a hot issue of common concern. In order to solve the problems of high misjudgment rate and low work efficiency in the current mental health intelligence evaluation process, a mental health intelligence evaluation system based on a joint optimization algorithm is proposed. The joint optimization algorithm consists of an improved decision tree algorithm and an improved ANN algorithm. First, analyze the current research status of mental health intelligence evaluation, and construct the framework of mental health intelligence evaluation system; then collect mental health intelligence evaluation data based on data mining, use joint learning algorithm to analyze and classify mental health intelligence evaluation data, and obtain mental health intelligence evaluation results. Finally, through specific simulation experiments, the feasibility and superiority of the mental health intelligent evaluation system are analyzed. The results show that the system in the article overcomes the shortcomings of the existing mental health intelligence evaluation system, improves the accuracy of mental health intelligence evaluation, and improves the efficiency of mental health intelligence evaluation. It has good system stability and can meet the actual current situation, which are requirements for mental health intelligence evaluation.

An Improved Decision Tree Algorithm for Condition Monitoring on Storage Power Station of Internet Things

Power station is an important basic power generation organization, and its operation status is related to the continuous power generation capacity. At present, a large number of physical network equipment and intelligent equipment are used in pumped storage power station, which makes its data mass growth and its operation state become a difficult problem. Accurate operation monitoring results can provide decision support that power generation planners and government, but also reasonably dispatch corresponding resources. In the past, decision tree algorithm was used in operation condition monitoring, which has the problem of data distortion and affects the accuracy of monitoring results. Based on the above reasons, this paper combines the wavelet function and decision tree algorithm, proposes an improved decision tree algorithm to eliminate redundant data in order, and uses wavelet function to cluster distorted data, so as to improve the accuracy and computational efficiency of the algorithm. Matlab simulation results show that: decision tree algorithm can eliminate 90% of redundant data, reduce the impact of feature data extraction on decision tree. At the same time, the improved accuracy is 98%, the calculation time is less than 25s is better than that, the decision tree algorithm. Therefore, the improved algorithm can optimize the condition monitoring of pumped storage power station.

Analysis and Simulation of Multimedia English Auxiliary Handle Based on Decision Tree Algorithm

In this paper, through the improved decision tree algorithm, the handles in multimedia English assistance are parsed and simulated. In order to better perceive the sense of language in English composition and improve the rationality of intelligent evaluation, an N element based on association analysis is proposed. Sense value quantification calculates its support in the corpus by obtaining N-tuples of the composition. If the degree of support is lower than the threshold, the part where the language sense problem occurs is analyzed, and the type of language sense problem is judged for the students to provide assistance in modifying the composition. In addition, this paper also extracts word features, sentence features, and text structure features in the composition to fit the English handles analytical score. By testing the test set, the experiment shows that, by extracting the language sense features of the candidate’s English composition, it can not only judge whether there is a problem with the language sense of the candidate, but also provide a basis for the overall evaluation of the composition.

Position Prediction Method Based on Improved Decision Tree

C4.5 decision tree algorithm is one of the commonly used classification prediction algorithms. It is a tree structure. Its advantage is that the process of obtaining results is easy to understand and the calculation is small, etc. Its disadvantage is that it is easy to cause over-fit, and will be very complex when there are too many categories. In the process of analyzing skill proficiency and positions, different job positions are available for different levels of skill proficiency. When there are more job categories, the prediction effect of decision tree classification is poor and still needs to be improved. In this paper, an improved decision Tree algorithm WF_D-tree is proposed, which adds skill proficiency weight to each data in the data table. Through the calculation method of skill proficiency weight, the relative redundant data of table data are removed. Through the longitudinal calculation method of skill proficiency weight, dimension reduction of data. Experimental results show that the improved decision tree greatly reduces the running time of the decision tree algorithm in the job prediction, and also improves the accuracy of the prediction results. In terms of time, the WF_D-tree is also significantly higher than the decision tree before the improvement. In terms of accuracy, the decision tree was improved by about 11 percentage points.

School enterprise cooperation mechanism based on improved decision tree algorithm

School enterprise cooperation is of great strategic significance to China’s manufacturing industry. The Party Central Committee has repeatedly proposed to strengthen the integration of industry and education and deepen the cooperation between schools and enterprises. In recent years, experts and scholars at home and abroad have been from different perspectives, although they have achieved fruitful results, there are also problems such as insufficient attention to theoretical research, relatively backward research, narrow research scope, and so on. In view of this phenomenon, this paper will study a new school-enterprise cooperation mechanism based on the improved decision tree algorithm. The research of this paper is divided into three parts. First, after analyzing the advantages and disadvantages of the algorithm, the algorithm of the decision tree is improved, which makes the improved algorithm more suitable for the field of school-enterprise cooperation. Then, based on cloud computing and intelligence, this paper establishes a new model of school-enterprise cooperation platform, which solves some problems of data management and information exchange in school-enterprise cooperation. Finally, in order to make the cooperation mechanism of this paper better used in practice, this paper builds an online and offline hybrid training base, hoping to make the cooperation between schools and enterprises closer through the training base. In order to test the effect of the cooperation model, this paper takes school as the experimental model. After investigation and research, it is believed that thanks to the school-enterprise cooperation mechanism in this paper, the cooperative enterprise of school has been greatly improved in the past three years, and the willingness of enterprises to cooperate has become more and more strong. No matter students, teachers, or enterprises are reaping huge benefits under this cooperation mechanism, it is a suit for extensive promotion The school-enterprise cooperation mechanism of Guangyun.