Traditional ID3 Algorithm Research Articles

Application and Optimization of Decision Tree ID3 Algorithm in Innovation and Entrepreneurship Analysis Model

In recent years, with the rise of innovation and entrepreneurship, the decision tree ID3 algorithm is more and more widely used in innovation and entrepreneurship analysis models. The decision tree ID3 algorithm is an effective data mining technology, which can mine useful information from a large number of data and provide a scientific basis for innovation and entrepreneurship. The traditional ID3 algorithm has obvious convergence characteristics, which limits its application in innovation and entrepreneurship analysis models. Therefore, it is important to optimize the ID3 algorithm to improve its computational efficiency and accuracy. From the perspective of the innovation and entrepreneurship analysis model, the application of the decision tree ID3 algorithm in innovation and entrepreneurship is studied. The basic principle, advantages, and disadvantages of the ID3 algorithm are analyzed, and a method to optimize the ID3 algorithm is proposed. Through the experimental verification, the application effect of the optimized ID3 algorithm in the analysis model of innovation and entrepreneurship is proved. When the number of records is 5200, the error rate of the optimization algorithm is 70%, while the error probability of the classical algorithm is 95%. When the number of records is 7200, the error rate of the optimized calculation method is 130%, while the error rate of the classic ID3 calculation method is 170%. As the number of records increases, the error rates of both optimization algorithms and classical algorithms will increase. As the number of records increases, the error rate of the optimized ID3 algorithm increases less than that of the classical algorithm. The optimized ID3 algorithm can significantly improve computational efficiency and accuracy and provide an effective data mining technology for innovation and entrepreneurship analysis models.

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

Abstract This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize the decision tree ID3 algorithm by using the data mining decision tree ID3 algorithm in the database of college students’ mental health assessment in the application of mental health assessment in colleges and universities as an example. Among them, the number of nodes, the number of rules, the classification accuracy and the time of constructing the decision tree of the algorithm are compared to verify the improvement effect of the ID3 algorithm. The target dataset consists of psychological assessment data of students, which includes their basic situation and nine-dimensional psychological symptoms. Analyze the recorded data of students’ mental health status, extract the information on personality, parental relationship, economic income, and psychological abnormality, set the decision tree analysis variables, assign the value of students’ health status, and derive the specific factors affecting students’ mental health by using the decision tree If-Then classification rules. The validation results show that the generated decision tree ID3 model cross-validation estimate is 0.261, the standard error is 0.016, and its obtained standard error is less than 0.018, which indicates that the model fits better.

Comprehensive Evaluation of College Students' Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model.

Nowadays, more and more college students' physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information loss rate is 4.136%, and the accuracy of motion mode decision is 92.58%. The average relative time is 12.7, and the accuracy of physical health decision making is 90.02%. The above two values are not significant compared with the traditional ID3 algorithm. The experimental results show that the improved ID3 algorithm has significant optimization in the stability of information transmission and the accuracy of sports recommendation decision making, and can be applied to the physical health evaluation and sports recommendation of college students in a certain range.

Financial Risk Analysis and Early Warning Research Based on Data Mining and Statistical Analysis Technology

Using the advantage of decision tree algorithm in the screening work, the traditional ID3 algorithm is improved and optimized, and a new and simplified financial index system is constructed. At the same time, combined with the unique value of artificial neural network in early warning model and data analysis, B-P model is used to build a mixed financial early warning model. In the model study, the HFPM model and Z-score model were compared and analyzed by using test samples and training samples, and the superior warning ability of the former was effectively verified.

Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application

The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a k‐nearest neighbor‐based ID3 optimization algorithm, which selects values similar to k‐nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the k‐nearest neighbor‐based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher‐performance online data migration model for sports competition action data mining.

Information classification algorithm based on decision tree optimization

With the rapid development of information technology, the efficiency of information management has drawn increasing importance with its broadening application. Hence, a new information classification algorithm has proposed in this paper so as to improve information management of the limited resources by reducing its complexity. However, ID3 algorithm is a classical and imprecise algorithm in data mining, because traditional ID3 algorithm selects the attribute that has the maximum information gain according to the data set as that of the split node. Then the data subset is further divided according to the number of attribute values, and the information gain of each subset is calculated recursively. Decision Tree Optimization Ratio is the core approach in this algorithm, whose basic ideas have been introduced and analysed, proving to be more complex. Therefore, the authors propose a relatively precise RLBOR algorithm which takes the number of nodes in the decision tree model into consideration. The experiment show more precise of RLBOR algorithm.

A Weather Forecasting Model using the Data Mining Technique

The weather conditions are changing continuously and the entire world is suffers from the changing Clemet and their side effects. Therefore pattern on changing weather conditions are required to observe. With this aim the proposed work is intended to investigate about the weather condition pattern and their forecasting model. On the other hand data mining technique enables us to analyse the data and extract the valuable patterns from the data. Therefore in order to understand fluctuating patterns of the weather conditions the data mining based predictive model is reported in this work. The proposed data model analyse the historical weather data and identify the significant on the data. These identified patterns from the historical data enable us to approximate the upcoming weather conditions and their outcomes. To design and develop such an accurate data model a number of techniques are reviewed and most promising approaches are collected. Thus the proposed data model incorporates the Hidden Markov Model for prediction and for extraction of the weather condition observations the K-means clustering is used. For predicting the new or upcoming conditions the system need to accept the current scenarios of weather conditions. The implementation of the proposed technique is performed on the JAVA technology. Additionally for justification of the proposed model the comparative study with the traditional ID3 algorithm is used. To compare both the techniques the accuracy, error rate and the time and space complexity is estimated as the performance parameters. According to the obtained results the performance of the proposed technique is found enhanced as compared to available ID3 based technique.

Predictive Modeling of Student Dropout Indicators in Educational Data Mining using Improved Decision Tree

Background/Objectives: Educational Data mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. The objective of this work is to identify relevant attributes from socio-demographic, academic and institutional data from undergraduate students at the university located in India and develop an improved decision tree algorithm based on ID3 which can able to predict whether the students continue or drop their studies. Methods/Statistical Analysis: The traditional ID3 algorithm is improved by using Renyi entropy, Information gain and Association Function and the model generated by improved decision tree algorithm may be beneficial for university administrators to create guidelines and policies related to raise the enrollment rate in university and to take precautionary and advisory measures and thereby reduce student dropout. It can also used to find the reasons and relevant factors that affect the dropout students. Findings: Experimental results proved that improved decision tree algorithm provides better prediction accuracy in educational data than that of traditional classification algorithms in the literature. Improvements/Applications: Improved decision algorithm was proposed that enhances the ability to form decision trees and thereby to prove that the classification accuracy of improved decision algorithm on educational dataset is greater.

Applications of Data Mining Algorithm in Equipment Fault Diagnosis

The Basic Principles of Data mining Decision-tree ID3 is opened out. The main deficiencies are analysed. An improved algorithm based on the ID3 is calculated. For fault diagnosis of engine exemple, traditional ID3 algorithm and the improved algorithm are applied to estimate the fault diagnosis of engine separately. Decision Trees of traditional ID3 algorithm and the improved algorithm are construct. Experiment result display the accuracy of improved algorithm is better than traditional ID3. The improved algorithm is more fit to applied to the equipment fault diagnosis.

Producing Security Policy of IPSec Based on Improved λ-ID3 Algorithm

IPSec is a policy-driven security mechanism. How to quickly generate corresponding security policy due to the diversity of network security is one of the core issues of IPSec. Although IPSec supports a rich set of protection modes and operations, its policy configuration remains an inefficient and error-prone task in the dynamic conditions. In this article, we propose an IPSec policy engine which can generate dynamically and store the security policy of IPSec under different algorithms. Furthermore, we extend a novel ID3 algorithm to automatically generate correct policies. The algorithm, which introduces the dynamic parameter-- importance factor λ when calculating the information entropy, can overcome the flaw of traditional ID3 algorithm that tends to choose attributes that have more values, and improve the efficiency and flexibility of IPSec security policy, and better meet the actual situation.