Application of Decision Tree ID3 Algorithm in Tax Policy Document Recognition

2021 ◽  
pp. 177-184
Author(s):  
Chao Pang
Keyword(s):  
Decision Tree ◽  
Tax Policy ◽  
Policy Document ◽  
Document Recognition ◽  
Id3 Algorithm

scholarly journals Penerapan Algoritma ID3 dalam Prediksi Kebutuhan Pupuk

2021 ◽  
Vol 2 (4) ◽  
pp. 247-253
Author(s):  
Milyani Aritonang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Plant Protection ◽  
Organic Fertilizer ◽  
Urea Fertilizer ◽  
Id3 Algorithm ◽  
Development Unit ◽  
Npk Fertilizer

The need for fertilizer at the Plant Protection Development Unit (UPPT) is uncertain depending on the demand of farmers, therefore it is necessary to predict fertilizer needs. There are five types of fertilizers predicted by the Plant Protection Development Unit (UPPT), including Urea fertilizer, ZA fertilizer, SP-36 fertilizer, NPK fertilizer, and Organic fertilizer, so fertilizer needs can be predicted. In predicting data mining on fertilizer needs using the ID3 algorithm. Where it works is calculating the value of entropy and gain to get the final result in the form of a tree to the decision and rule. Testing is done using the tanagra software. The results of the tests carried out on the tanagra application using the ID3 algorithm are in the form of a decision tree, while in the calculation the results obtained are in the form of a decision tree.

Decision Tree-Based Analytics for Reducing Air Pollution

2018 ◽  
Vol 17 (02) ◽  
pp. 1850015 ◽  
Author(s):  
Ajanta Das ◽  
Anindita Desarkar
Keyword(s):  
Air Pollution ◽  
Decision Tree ◽  
World Health ◽  
Pollution Level ◽  
Rule Base ◽  
Premature Deaths ◽  
Id3 Algorithm ◽  
Rule Sets ◽  
Term Basis ◽  
Health Organization

Air pollution indicates contaminated air which arises due to the effect of physical, biological or chemical alteration to the air in the atmosphere applicable both for indoors and outdoors. This situation arises when poisonous gases, dust or smoke enter into the atmosphere and make the surroundings vulnerable for any living beings as well as difficult for them to survive. Large numbers of premature deaths happen across the globe if exposed to these pollutants on a long-term basis as major portion of the cities have the pollution level above the threshold determined by World Health Organization (WHO). So appropriate measures need to be taken on a priority basis to reduce air pollution as well as save our planet. This paper proposes a novel air pollution reduction approach which collects source pollution data. After extraction of source data, it uses various databases (DBs) and then different decisions or classes are created. The decision tree was created with the help of Iterative Dichotomiser 3 (ID3) algorithm to implement the rule base appropriately depending on the air pollution level and a bunch of rule sets were derived from the decision tree further.

A decision tree using ID3 algorithm for English semantic analysis

2017 ◽  
Vol 20 (3) ◽  
pp. 593-613 ◽  
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran ◽  
Vo Thi Ngoc Chau ◽  
Nguyen Duy Dat ◽  
Khanh Ly Doan Duy
Keyword(s):  
Decision Tree ◽  
Semantic Analysis ◽  
Id3 Algorithm

scholarly journals Developed third iterative dichotomizer based on feature decisive values for educational data mining

2020 ◽  
Vol 18 (1) ◽  
pp. 209
Author(s):  
Saja Taha Ahmed ◽  
Rafah Al-Hamdani ◽  
Muayad Sadik Croock
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Decision Tree ◽  
Predictive Analytics ◽  
Educational Data Mining ◽  
Target Class ◽  
Id3 Algorithm ◽  
Feature Weight ◽  
Holdout Validation ◽  
Fold Cross Validation

<p><span>Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement. <br /> The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT) is introduced and constructed based on a decisive feature value as a feature weight related to the target class label. The traditional Iterative Dichotomizer 3 (ID3) algorithm and the proposed DDT are compared using three datasets in terms of some ID3 issues, including logarithmic calculation complexity and multi-values features<em></em>selection. The results indicated that the proposed DDT outperforms the ID3 in the developing time. The accuracy of the classification is improved on the basis of 10-fold cross-validation for all datasets with the highest accuracy achieved by the proposed method is 92% for the student.por dataset and holdout validation for two datasets, i.e. Iraqi and Student-Math. The experiment also shows that the proposed DDT tends to select attributes that are important rather than multi-value. </span></p>

scholarly journals PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET

2017 ◽  
Vol 5 (8) ◽  
pp. 260-266
Author(s):  
Subhankar Manna ◽  
Malathi G.
Keyword(s):  
Decision Tree ◽  
Metabolic Diseases ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set ◽  
Id3 Algorithm

Healthcare industry collects huge amount of unclassified data every day.  For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using R package. This work used the dataset that is imported from the UCI Machine Learning Repository, Diabetes 130-US hospitals for years 1999-2008 Data Set. Motivation/Background: Naïve Bayes is a probabilistic classifier based on Bayes theorem. It provides useful perception for understanding many algorithms. In this paper when Bayesian algorithm applied on diabetes dataset, it shows high accuracy. Is assumes variables are independent of each other. In this paper, we construct a decision tree from diabetes dataset in which it selects attributes at each other node of the tree like graph and model, each branch represents an outcome of the test, and each node hold a class attribute. This technique separates observation into branches to construct tree. In this technique tree is split in a recursive way called recursive partitioning. Decision tree is widely used in various areas because it is good enough for dataset distribution. For example, by using ID3 (Decision tree) algorithm we get a result like they are belong to diabetes or not. Method: We will use Naïve Bayes for probabilistic classification and ID3 for decision tree.  Results: The dataset is related to Diabetes dataset. There are 18 columns like – Races, Gender, Take_metformin, Take_repaglinide, Insulin, Body_mass_index, Self_reported_health etc. and 623 rows. Naive Bayes Classifier algorithm will be used for getting the probability of having diabetes or not. Here Diabetes is the class for Diabetes data set. There are two conditions “Yes” and “No” and have some personal information about the patient like - Races, Gender, Take_metformin, Take_repaglinide, Insulin, Body_mass_index, Self_reported_health etc. We will see the probability that for “Yes” what unit of probability and for “No” what unit of probability which is given bellow. For Example: Gender – Female have 0.4964 for “No” and 0.5581 for “Yes” and for Male 0.5035 is for “No” and 0.4418 for “Yes”. Conclusions: In this paper two algorithms had been implemented Naive Bayes Classifier algorithm and ID3 algorithm. From Naive Bayes Classifier algorithm, the probability of having diabetes has been predicted and from ID3 algorithm a decision tree has been generated.

SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG KORONER DENGAN METODE PROBABILISTIC FUZZY DECISION TREE

2018 ◽  
Vol 4 (2) ◽  
pp. 106
Author(s):  
Wizra Aulia
Keyword(s):  
Blood Pressure ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Chest Pain ◽  
Expert System ◽  
Decision Tree ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Id3 Algorithm ◽  
Anticipatory Action

<p><em>Di Indonesia, penyakit jantung koroner menempati posisi pertama sebagai penyakit yang paling banyak mengakibatkan kematian. Jika gejala penyakit jantung koroner  dikenali sejak dini maka dapat dilakukan tindakan antisipasi. Diagnosa dilakukan berdasarkan 6 gejala penyakit jantung koroner yaitu sakit dada, tekanan darah tinggi, kolesterol, kadar gula darah, hasil EKG dan jumlah denjut jantung. Metode yang dipakai adalah Probabilistic Fuzzy Decision Tree (PFDT) dengan algoritma  Probabilistic Fuzzy  ID3. Hasil keakuratan sistem pakar diagnosa penyakit jantung koroner dengan metode PFDT mencapai 95%.</em><em></em></p><p><em>In Indonesia, coronary heart disease the first position as the disease that most resulted in death. If symptoms of coronary heart disease are recognized early on, anticipatory action may be taken. Diagnosis is based on 6 symptoms of coronary heart disease  chest pain, high blood pressure, cholesterol, blood sugar, ECG results and </em>heartbeat<em>. The method used is Probabilistic Fuzzy Decision Tree (PFDT) with Probabilistic Fuzzy ID3 algorithm. The result of accuracy of expert system of diagnosis of coronary heart disease by PFDT method reached 95%.</em></p>

Identification of Factors Affecting Disk Drive’s Performance in Data Server by Use of Decision Tree Learning Method

2021 ◽  
Author(s):  
Yi-Ju Liao ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Learning Method ◽  
Training Set ◽  
Decision Tree Learning ◽  
Maximum Information ◽  
Factors Affecting ◽  
Id3 Algorithm ◽  
Maximum Information Entropy ◽  
Smart Machine

Abstract To identify factors affecting magnetic disk drive’s data recording performance in data server, decision tree learning method is proposed and validated in this paper. Aiming at improving classification efficiency of various causes of HDD performance degradation, the ID3 algorithm of decision tree was first used showing the training set model would be able to achieve 100% accuracy. The maximum information entropy and information gain theory of ID3 algorithm were then adopted, from which accuracy range of 0.5–0.6 can be further achieved. The proposed method was validated to be effective for leveraging the data sever into Industry 4.0 ready smart machine.

Analysis of Customer Behavior in Automatic Investment Plan Based on Decision Tree

2014 ◽  
Vol 644-650 ◽  
pp. 5741-5744
Author(s):  
Li Huo ◽  
Bo Jiang ◽  
Ya Xin Liu
Keyword(s):  
Decision Tree ◽  
Service Quality ◽  
Marketing Strategy ◽  
Customer Behavior ◽  
Investment Plan ◽  
Id3 Algorithm ◽  
Basic Ideas ◽  
Improve Service Quality

Firstly, this paper introduces the decision tree and then the basic ideas and implementation methods of ID3 algorithm are discussed. Next, processes the data of customer behavior in the AIP business based on ID3 and constructs the decision tree meanwhile the rules are established. Finally, gives the results of the prediction and advices on how to design marketing strategy of fund business. This study can be used to make marketing strategy for fund companies and improve service quality of fund companies consequently.

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