ID3 Method Research Articles

The Impact of the Big Thinking Program on Students’ Mental Health in the Context of Three-Way Parenting

Abstract This paper combines the stepwise regression algorithm as well as the decision tree algorithm to design a decision tree stepwise regression algorithm model of the impact of the big Civics program on students’ mental health. This paper focuses on the use of a stepwise regression algorithm to eliminate the independent variables that do not have a significant impact on the dependent variable, and through the decision tree, ID3 method to test all the features, in-depth exploration of the degree of influence of the large courses of ideology and politics on students’ mental health. In order to be able to more deeply solve the degree of influence of the large course of thought and politics on students’ mental health, a oneway linear regression analysis is carried out on the basis of the relevant analysis data of students’ mental health. The results show that the histogram of the independent variable of the large course of ideology and the dependent variable of the total mean score is distributed in a normal curve, with a mean of 2.82E-16, a standard deviation of 0.999, and a correlation coefficient squared of 0.057, which indicates that the large course of ideology has a predictive effect on the mental health of students and that the students’ anorexia mental health is influenced by the large course of ideology by 0.057. This study shows that the large course of ideology is able to promote the physical and mental health development of students and contribute to the mental health of the students. This study shows that the large ideology and politics program can promote the healthy development of student’s physical and mental health, provide a direction for mental health education to become an effective carrier of “moral education”, and also provide a breakthrough point for the reform and innovation of students’ mental health education.

A Review on Classification of Various Types of Decision Trees with Merits and Demerits

Data mining includes several important technologies such as classification, clustering, regression, etc. Categorical mining technology, among data mining technologies, is becoming the most mature and active research direction enabling successful applications. Categorical mining can be applied to uncover useful information from large amounts of data stored in a large number of fields such as hospitals, inventory, banking, etc. Decision tree methods, neural network methods and statistics exploratory methods. In this paper a large number of decision tree algorithms such as Iterative Dichotomizer 3, CART (Classification and Regression Tree), C4.5, SLIQ and SPRINT are used in different fields. These algorithms have been explained clearly with merits and demerits along with some applications. Most of the decision tree methods are developed from the ID3 method.

Data Mining Integration with PostgreSQL Extension by K-Means, ID3 and 1R Method

Data mining is a tool that allows users to quickly access large amounts of data. The purpose of this study was to analyze the integration of data mining technique algorithms into the PostgreSQL database management system. The method used in this research is K-Means, ID3 and 1R, the tools used to implement data mining using RapidMiner and PostgreSQL tools. In this study, the number of rows to be analyzed is 100,000 records, 500,000 records, and 1,000,000 records. The results obtained are the algorithm implemented to validate the data by using an experimental design that serves to observe the time that the analysis of the algorithm that has been integrated into the DBMS is smaller than the results from Rapidminer. As the number of records increases, data analysis becomes difficult using RapidMiner. Data mining techniques, Database management system, Partition, Response time

An English Reading and Learning System Based on Web

Learning management system (LMS) is a web-based system used to develop, implement, and assess a specific learning process. When designing an administration learning system, it is important to understand the general architecture and roles of each level of the system. In this study, we propose a web-based English reading learning system to address the drawbacks of existing methods in English learning resource management that include login impact, system response time, and user satisfaction. English reading materials are integrated using a feature model, and the adaptive recommendation system is built using the ID3 method. The design of the proposed English reading learning system was intended to employ a hybrid of software and hardware that will benefit a wide variety of groups, especially universities, colleges, and organizations. The statistic results showed that the proposed model is capable of meeting the functional objectives of English learning resource management, as well as having a rapid and accurate response time with high user satisfaction.

Komparasi Criteria Splitting Pada Algoritma Iterative Dichotomizer 3(ID3) Untuk Klasifikasi Kelayakan Kredit

Credit is a form of business run by various banks. In running credit, banks will always consider the credit that will occur, for that data about creditworthiness decisions will be very necessary to support the business wheels of banking life. Credit analysis is carried out to support credit or historical data. This can reduce credit. Bank Sinarmas Sukabumi is one of the largest finance companies in Indonesia. This company provides financing services for the purchase of new vehicles or used vehicles, some funds and funds provided by Sinarmas Bank in terms of credit, credit assistance, one for analyzing important credit, because one of them, looking for credit, bad things that can be done by companies that are not careful in granting credit. The ID3 algorithm can search all discussions and decisions. The application of the ID3 method by comparing the three criteria for obtaining credit, understanding the assessment criteria, obtaining an accuracy value of 62.67% and an AUC value of 0.800, the highest in accordance with the criteria being compared, is discussed with the criteria. The Strengthening Ratio and Gini Index have the lowest Accuracy and AUC values. Thus, the ID3 method with Gain Information criteria is a good method and criterion in predicting lending to Bank Sinarmas Sukabumi .

SISTEM PENUNJANG KEPUTUSAN PEMILIHAN BEASISWA DENGAN METODE DECISION TRE ID3 PADA SMAN 2 CIBINONG KABUPATEN BOGOR

The selection of scholarships must be in accordance with established rules. The criteria set out in this case study are academic values, parents' income, number of siblings, number of dependents of parents, active organizations and others. Therefore not all who register as candidates for the scholarship will be accepted, only those who meet the criteria will get the scholarship. Because of the large number of participants who apply for scholarships, it is necessary to build a decision support system that will help determine who is entitled to get the scholarship. Therefore, this study seeks to provide a solution for the selection of students who deserve a scholarship in accordance with the criteria that have been determined by each to produce more accurate and faster data. Decision Tree ID3 method as a comparative test analysis model, as well as policy analysis is used to support appropriate decision making for the school board. ID3 Decision Tree is suitable for use in this case because it can make data classification accuracy and tree generated results are very easy to read by humans.

PENERAPAN METODE ITERATIVE DICHOTOMIZER (ID3) UNTUK DIAGNOSA HAMA TANAMAN ANGGREK

Orchid plants is one component of the aesthetic aspect and become a part of human life. Diagnostics system for classification orchid pests were attack plants still using the manual model, it’s result difficulty identifying pests orchid that has a variety of symptoms are almost the same. As to distinguish the symptoms of damage to the leaves, roots and flowers caused by pests. ID3 able to generate a decision tree of large data sets. This decision tree can be used as a reference for the diagnosis of pests on orchids. In this research, pest classification of orchids do with ID3 method with the highest level of performance accuracy is 78.06%.

A Compendium on the Contrasting Features of Data Mining Algorithms for the Diagnosis of Diabetes

Data mining technology offers a user-oriented technique to hidden and novel patterns in the data. Because of rising diseases in the world, we are unable to evaluate all types of diseases and how to consume correct medicine for various diseases. Techniques of data mining are quite useful in finding the medicinal decision for the suitable diseases. Diabetes monitoring system is beneficial to diabetes patients. The diabetes system is useful not just for diabetes patients but also for those suspecting they are diabetic. The primary goal of this paper is to conduct a comparative analysis of decision tree algorithms namely ID3 and CART method focusing on diagnosis of diabetes.

Classification of image objects in Epo doping control using fuzzy decision tree

Erythropoietin (Epo) is a hormone which can be misused as a doping substance. Its detection involves analysis of images containing specific objects (bands), whose position and intensity are critical for doping positivity. Within a research project of the World Anti-Doping Agency (WADA) we are implementing the GASepo software that serves for Epo testing in doping control laboratories worldwide. For identification of the bands we have developed a segmentation procedure based on a sequence of filters. Whereas all true bands are properly segmented, the procedure generates a number of false positives (artefacts). To separate these artefacts we suggested a post-segmentation supervised classification using real-valued geometrical measures of objects. The method is based on a fuzzy modification of Ross Quinlan’s ID3 method, included in the mlf™ software (Machine Learning Framework). It provides a framework that generates fuzzy decision trees, as well as fuzzy sets for input data. Initially used training set of segmented objects has been replaced by a new one prepared by more accurate expertise using the latest release of the GASepo software. The new fuzzy decision trees (FDT) have been generated for a set of five and nine fuzzy sets. The comparison of the results on testing set of segmented objects shows that the classification based on the new FDTs outperforms other classification methods.

K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods

In this paper, we present k-means+ID3, a method to cascade k-means clustering and the ID3 decision tree learning methods for classifying anomalous and normal activities in a computer network, an active electronic circuit, and a mechanical mass-beam system. The k-means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final decision on classification, the decisions of the k-means and ID3 methods are combined using two rules: 1) the nearest-neighbor rule and 2) the nearest-consensus rule. We perform experiments on three data sets: 1) network anomaly data (NAD), 2) Duffing equation data (DED), and 3) mechanical system data (MSD), which contain measurements from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical system, respectively. Results show that the detection accuracy of the k-means+ID3 method is as high as 96.24 percent at a false-positive-rate of 0.03 percent on NAD; the total accuracy is as high as 80.01 percent on MSD and 79.9 percent on DED

Sistema inteligente para protocolizar el diagnóstico de invasión ganglionar en el cáncer de laringe

Laryngeal carcinoma is the most frequent malignant tumour in head and neck. Node invasion is known to be one of the most important prognostic factors. The aim of this study has been to design an intelligent system to perform a diagnostic algorithm of metastasic neck nodes. 122 clinical reports of patients diagnosed of laryngeal carcinoma in our department have been reviewed. The compiled data have been: tumor site, T stage, N stage (clinical, after CT scan and post-surgery). The method used to design the intelligent system has been the ID3, which is able to generate a minimal decision tree. Palpation has been the variable that has given more information about node invasion. CT has proved to be more efficient in supraglottic tumours. ID3 method has shown to be useful in performing diagnostic algorithms, specially when the number of cases and diagnostic tests are high.

The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods

This is a comparative study of inductive learning and statistical methods using the simulation approach to provide a generalizable results. The purpose of this study is to investigate the impact of measurement scale of explanatory variables on the relative performance of the statistical method (probit) and the inductive learning method (ID3) and to examine the impact of correlation structure on the classification behavior of the probit method and the ID3 method. The simulation results show that the relative classification accuracy of ID3 to probit increases as the proportion of binary variables increases in the classification model, and that the relative accuracy of ID3 to probit is higher when the covariance matrices are unequal among populations than when the covariance matrices are equal among populations. The empirical tests on ID3 reveal that the classification accuracy of ID3 is lower when the covariance matrices are unequal among populations than when the covariance matrices are equal among populations and that the classification accuracy of ID3 decreases as the correlations among explanatory variables increases.

A Machine Learning Approach for a Manufacturing Database of Nb-Ti Superconducting Wire.

This paper describes a machine learning approach for a manufacturing database in a Nb-Ti superconducting wire domain. A Nb-Ti superconducting wire is produced by iterating the drawing and heat treatment operations. A problem to determine a production schedule, or the parameters in the drawing and heat treatment operations, is discussed. We first show a database approach that selects the most promising schedule to achieve the target superconducting quality from the data. This aproach is useful in the effective reuse of the past experiences, but does not yield a novel production schedule Next we present a machine learning approach to extract knowledge to improve a production schedule for better superconducting quality. We use ID3 method to generalize the data, and we describe two criteria, correctness and applicability indices, to measure the quality of the induced knowledge.

Automatic learning of chemical concepts: Research octane number and molecular substructures

A set of 230 hydrocarbons is analyzed with respect to the research octane number (RON) using an extended version of the ID3 machine learning method of Quinlan. The basic ID3 method produces a decision tree. The questions within the decision tree ask whether given substructures are present or absent within a molecule (for example, is the hexane carbon skeleton present as a substructure?). The decision to be made is whether the RON value is within a given range (for example, is the RON greater than or equal to 90). In addition to the normal usage of decision trees to predict the RON value, qualitative information is extracted: for example, which substructures are significant in which RON ranges. The best results were found for those molecules having RON values between 90 and 105. This was due to the high density of samples in this region. The ID3 method is used to prune down an overabundance of parameters to a reasonable size. From the original set of 230 substructures, it was found that only 31 were needed for the RON description. The presence or absence of the substructures n-hexane, methylbenzene and benzene were particularly significant in determining the RON value. Considering that ID3 is not a traditional method of analysis, especially within chemistry, an effort is made to explain its principles using the problem at hand as an example. In addition, its use as the first step in analysis to gain intuitive information is explained.