scholarly journals DEVELOPING AN INTEGRATED MODEL BASED ON NAÏVEBAYES AND DECISION TREE ALGORITHMS IN THE EARLY DETECTION AND DIAGNOSIS OF CARDIAC DISEASES

2021 ◽  
Vol 12 (01) ◽  
Author(s):  
Ishaan Gupta
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Sets ◽  
Information Mining ◽  
Tree Algorithms ◽  
Detection And Diagnosis ◽  
Concealed Information ◽  
Early Detection And Diagnosis

The extraction of concealed information from the enormous data sets is information mining, and it is otherwise called Knowledge Discovery Mining. It has many assignments. One of them utilized here is prescient errands that use a few factors to foresee obscure or future upsides of another dataset. The significant medical issue that influences countless individuals is a coronary illness. Except if it is treated at a beginning phase, it causes demise. Today, the Healthcare business creates an enormous measure of perplexing information about the patients and assets of the emergency clinics, from a period where there has been no good spotlight on compelling examination instruments to find connections in communication, particularly in the clinical area. The methods of mining information are utilized to examine rich assortments of details according to alternate points of view and infer useful data to foster analysis and anticipating frameworks for coronary illness dependent on prescient mining. Various preliminaries are taken up to look at the exhibitions of different information mining procedures, including Decision trees and Naïve Bayes calculations. As proposed, the peril factors are pondered, Decision trees and Naïve Bayes are applied, and the show of their finding have been investigated by the UCI Machine Learning Repository I,e WEKA instrument. Thusly, the Naïve Bayes beats the Decision tree.

scholarly journals A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

2021 ◽  
Author(s):  
Yannick Kiffen ◽  
Francesco Lelli ◽  
Omid Feyli
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Related Result ◽  
Naïve Bayes ◽  
Student Enrollment ◽  
Single Application ◽  
Ve Bayes ◽  
Tree Algorithms ◽  
C5.0 Decision Tree

In this preprint, we introduce a dataset containing students enrolment applications combined with the related result of their filing procedure. The dataset contains 73 variable. Student candidates, at the time of applying for study, fill a web form for filing the procedure. A committee at Tilburg University review each single application and decide if the student is admissible or not. This dataset is suitable for algorithmic studies and has been used in a comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms. They have been used for predicting the decision of the committee in admitting candidates at various bachelor programs. Our analysis shows that, in this particular case, a combination of the approaches outperform a both of them in term of precision and recall.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

2019 ◽  
Vol 64 (2) ◽  
pp. 53-71
Author(s):  
Botond Benedek ◽  
Ede László
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Naive Bayes ◽  
Insurance Industry ◽  
Naïve Bayes ◽  
Sql Server ◽  
Categorical Variables ◽  
Automobile Insurance ◽  
Price Determination ◽  
Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

Human activity classification using Decision Tree and Naïve Bayes classifiers

2021 ◽  
Author(s):  
Kholoud Maswadi ◽  
Norjihan Abdul Ghani ◽  
Suraya Hamid ◽  
Muhammads Babar Rasheed
Keyword(s):  
Decision Tree ◽  
Human Activity ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Activity Classification

IMPRECISE CLASSIFICATION WITH CREDAL DECISION TREES

2012 ◽  
Vol 20 (05) ◽  
pp. 763-787 ◽  
Author(s):  
JOAQUÍN ABELLÁN ◽  
ANDRÉS R. MASEGOSA
Keyword(s):  
Decision Trees ◽  
Naive Bayes ◽  
Cardinal Number ◽  
Naïve Bayes ◽  
Classification Method ◽  
Cost Matrix ◽  
Imprecise Dirichlet Model ◽  
Dirichlet Model

In this paper, we present the following contributions: (i) an adaptation of a precise classifier to work on imprecise classification for cost-sensitive problems; (ii) a new measure to check the performance of an imprecise classifier. The imprecise classifier is based on a method to build simple decision trees that we have modified for imprecise classification. It uses the Imprecise Dirichlet Model (IDM) to represent information, with the upper entropy as a tool for splitting. Our new measure to compare imprecise classifiers takes errors into account. Thus far, this has not been considered by other measures for classifiers of this type. This measure penalizes wrong predictions using a cost matrix of the errors, given by an expert; and it quantifies the success of an imprecise classifier based on the cardinal number of the set of non-dominated states returned. To compare the performance of our imprecise classification method and the new measure, we have used a second imprecise classifier known as Naive Credal Classifier (NCC) which is a variation of the classic Naive Bayes using the IDM; and a known measure for imprecise classification.

scholarly journals Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Qingchao Liu ◽  
Jian Lu ◽  
Shuyan Chen ◽  
Kangjia Zhao
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classifier Ensemble ◽  
Optimal Threshold ◽  
Incident Detection ◽  
Bayes Classifier ◽  
Traffic Incident ◽  
Better Than

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

Sign in / Sign up

Export Citation Format

Share Document