Adaptive Multimode Decision Tree Classification Model Using Effective System Analysis in IDS for 5G and IoT Security Issues

2021 ◽  
pp. 141-158
Author(s):  
P. T. Kalaivaani ◽  
Raja Krishnamoorthy ◽  
A. Srinivasula Reddy ◽  
Anand Deva Durai Chelladurai
Keyword(s):  
Decision Tree ◽  
System Analysis ◽  
Classification Model ◽  
Iot Security ◽  
Security Issues ◽  
Decision Tree Classification ◽  
Effective System

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

2006 ◽  
Vol 05 (01) ◽  
pp. 227-240 ◽  
Author(s):  
N. REN ◽  
M. ZARGHAM ◽  
S. RAHIMI
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Security Analysis ◽  
Predictive Performance ◽  
Classification Model ◽  
Stock Selection ◽  
Selection Rules ◽  
Stock Portfolios ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

scholarly journals PENERAPAN ALGORITMA C4.5 UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT (Studi Kasus : Koperia - Koperasi Warga Komplek Gandaria)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Teguh Budi Santoso ◽  
Dela Sekardiana
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Model ◽  
Decision Tree Classification ◽  
C4.5 Algorithm ◽  
Credit Worthiness ◽  
Loan Amount

<p><em>Current credit giving in KOPERIA (Koperasi Warga Komplek Gandaria) is still based on an objective process. Difficulties in determining the feasibility of giving credit are often experienced by cooperative managers, so that problems arise in the cooperative is a default payment of credit installments of customers in KOPERIA. This study aims to form a decision tree classification model to determine the customer's credit worthiness. In this study the application of C4.5 Algorithm, based on the Sets and Attributes used in this study, namely, the amount of income divided into 2 categories&gt; 5 million and 3-5 million, the amount of balance divided into three, namely&gt; 3 million, 1-3 million and &lt;1 Million, The Loan Amount is divided into three, namely 1-4 Months, 5-8 months, and 9-12 Months and Requirements with attributes of Business Capital, buying goods and others. In this study determine the appropriate root nodes, the classification results using C4.5 Algorithm shows that the accuracy of 97.5% is obtained, based on the results obtained shows that the c4.5 algorithm is suitable to be used to determine the feasibility of lending customers to KOPERIA.</em></p><p><strong><em>Keywords</em></strong><em>: Data Mining, C4.5 Algorithm</em><em>, loan feasibility</em></p>

scholarly journals Decision Tree Classification Model for Popularity Forecast of Chinese Colleges

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xiangxiang Zeng ◽  
Sisi Yuan ◽  
You Li ◽  
Quan Zou
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Classification Model ◽  
Prospective Students ◽  
Final Model ◽  
Decision Tree Classification ◽  
Tree Construction ◽  
Tree Models ◽  
Educational Field ◽  
Existing Data

Prospective students generally select their preferred college on the basis of popularity. Thus, this study uses survey data to build decision tree models for forecasting the popularity of a number of Chinese colleges in each district. We first extract a feature called “popularity change ratio” from existing data and then use a simplified but efficient algorithm based on “gain ratio” for decision tree construction. The final model is evaluated using common evaluation methods. This research is the first of its type in the educational field and represents a novel use of decision tree models with time series attributes for forecasting the popularity of Chinese colleges. Experimental analyses demonstrated encouraging results, proving the practical viability of the approach.

scholarly journals PERANCANGAN APLIKASI PREDIKSI LAMA STUDI MAHASISWA BERDASARKAN MODEL KLASIFIKASI DATA LAMA STUDI MAHASISWA STMIK INDONESIA YANG TERBENTUK MENGGUNAKAN DECISION TREE

Petir ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Decision Tree ◽  
Bachelor's Degree ◽  
Classification Model ◽  
Degree Programs ◽  
University Staff ◽  
Decision Tree Classification ◽  
Study Duration ◽  
Student’S Performance ◽  
The University ◽  
Predict Study

One way to improve quality in universities is through accreditation. One of the accreditation criteria is the student. Student’s performance must be monitored and evaluated. Regarding the study duration, the undergraduate bachelor’s degree programs typically takes four years to complete. It is important for the university staff to quickly identify which students are less likely to finish the degree on time. Therefore, it is necessary to predict the length of study for each student. The goal of this research is to predict study duration by building Decision Tree-based classifier model using NBTree algorithm. Then, an application is built by applying the classification model. Data used in this research are the grades and academic leave. Result shows that the Naïve Bayes Decision Tree classification model could predict study duration with the accuracy of 73.45%.

scholarly journals Handwritten digits recognition with decision tree classification: a machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 4446
Author(s):  
Tsehay Admassu Assegie ◽  
Pramod Sekharan Nair
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Learning Approach ◽  
Data Sets ◽  
The Past ◽  
Decision Tree Classification ◽  
Machine Learning Approach ◽  
Future Data ◽  
Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

scholarly journals Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1531
Author(s):  
ZhenHua Li ◽  
Yujie Zhang ◽  
Ahmed Abu-Siada ◽  
Xingxin Chen ◽  
Zhenxing Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Classification Model ◽  
Response Analysis ◽  
Decision Tree Classification ◽  
Lumped Parameter ◽  
Proposed Model ◽  
Industry Practice ◽  
Transformer Model ◽  
Fully Connected

While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.

scholarly journals Efficient data cleaning algorithm using decision tree classification model approach and modified new unique user identification algorithm using hashing techniques with a new error factor

2018 ◽  
Vol 7 (1.9) ◽  
pp. 54
Author(s):  
Ranjena Sriram ◽  
S Sheeja ◽  
I Henry Alexander
Keyword(s):  
Decision Tree ◽  
United Arab Emirates ◽  
Data Cleaning ◽  
Classification Model ◽  
Identification Algorithm ◽  
User Identification ◽  
Web Log ◽  
Decision Tree Classification ◽  
Error Factor ◽  
Efficient Data

The study focuses on preprocessing techniques of web mining. Considering this scope, the study has proposed and implemented an efficient data cleaning and unique user identification algorithms. Previously proposed data cleaning algorithm is a generalized approach and lacked transparency. An appropriate model has to be used to implement the new data cleaning algorithm. Over analysis of various related studies and suggestions made by eminent experts, the study finalized decision tree classification model, and appropriate model to implement the new data cleaning algorithm. Simplicity, ease in framing rules and ability to fragment complex decisions to solve a problem motivated to choose decision tree classification model to implement new data cleaning algorithm. Apart from this the study has also modified the previously proposed hash function, used to locate existing web users in web log server. A new error factor is introduced to remove memory address discrepancy. The modified hashing function along with binary search techniques is used to design the new unique user identification algorithm. Various experiments analysis is done using web log servers of eminent universities and colleges from United Arab Emirates and India. Results obtained prove the improved and better performances of the new rule based data cleaning and modified unique user identification algorithms.

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