Analysis of Imputation Methods of Small and Unbalanced Datasets in Classifications using Naïve Bayes and Particle Swarm Optimization

2020 ◽  
Author(s):  
Muhammad Misdram ◽  
Edi Noersasongko ◽  
Abdul Syukur ◽  
Purwanto ◽  
Muljono Muljono ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Imputation Methods

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

scholarly journals SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION

2020 ◽  
Vol 2 (3) ◽  
pp. 169-178
Author(s):  
Zulia Imami Alfianti ◽  
Deni Gunawan ◽  
Ahmad Fikri Amin
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Swarm Optimization ◽  
Advantages And Disadvantages ◽  
Improve Accuracy

Sentiment analysis is an area of ​​approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions

scholarly journals ANALISA NASABAH POTENSIAL TABUNGAN DEPOSITO BERJANGKA MENGGUNAKAN TEKNIK KLASIFIKASI DATA MINING

2019 ◽  
Vol 5 (2) ◽  
pp. 105-112
Author(s):  
Candra Agustina
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Financial Institution ◽  
Unified Modeling Language ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Time Deposit ◽  
Bayes Algorithm ◽  
Potential Customers

Time deposits are a product of a financial institution, which is currently increasing. The main target of this time deposit marketing is the old customers of the Bank. To increase the effectiveness of marketing customers are grouped into potential and non-potential customers. This means that potential customers have a greater chance to open a time deposit account. Customer data is taken from the UCI repository, originating from Banks in Portugal. Data is processed with rapidminer software using the Decision Tree method with Particle Swarm Optimization, Naïve Bayes with Particle Swarm Optimization and finally processed using Neural Network with Particle Swarm Optimization. Data processing results were compared and showed that the Naïve Bayes Algorithm with Particle Swarm Optimization had the highest accuracy of 97.04%. Therefore an application designed based on Naive Bayes with Particle Swarm Optimization. From the original attribute consisting of 20, only 9 attributes can be used so that the level of accuracy is high. Attributes used have values ​​more than 0.500, while those that have these values ​​are omitted. The design was created using the Unified Modeling Language (UML) and Visual Basic 6.0 to create an User Interface.

scholarly journals Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes

2019 ◽  
Vol 9 (2) ◽  
pp. 97
Author(s):  
Firman Tempola
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Attribute Weights ◽  
The Status ◽  
Bayes Algorithm

<p class="JGI-AbstractIsi">This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.</p>

scholarly journals Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andre Alvi Agustian ◽  
Achmad Bisri
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Credit Card ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Class Imbalance ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
The Status ◽  
Auc Value

Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944.

scholarly journals EMPLOYEE TURNOVER CLASSIFICATION USING PSO-BASED NAÏVE BAYES AND NAÏVE BAYES ALGORITHM IN PT. MASTERSYSTEM INFOTAMA

2021 ◽  
Vol 3 (3) ◽  
pp. 233-240
Author(s):  
Endang Sri Palupi
Keyword(s):  
Particle Swarm Optimization ◽  
Employee Engagement ◽  
Employee Turnover ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Business World ◽  
New Employees ◽  
Bayes Algorithm

Turnover occurs because many employees leave and new employees enter, so the turnover in and out of employees is quite high, therefore turnover can be controlled with a strategy to increase employee engagement. PT. Mastersystem Infotama is a System Integrator company or better known as a fairly large IT company with a total of approximately 600 employees. Turnover is high enough to make some divisions lack human resources, and the human capital management division is quite difficult to recruit employees to find candidates with various criteria that must be available in a short time. Competition in the IT world is quite tight both within companies and employees with good experience and abilities. Especially the sales department that holds a database of potential customers, and the engineer section that already has a certificate of expertise that is widely used in the IT business world. Therefore, it is necessary to classify what factors make employee turnover high by using the Naïve Bayes and Naïve Bayes algorithms based on Particle Swarm Optimization, so that they can be used as material for internal evaluation to increase employee engagement. The results of this study, classification using the Naïve Bayes algorithm, has an accuracy of 79.17%, while the classification using the Naïve Bayes algorithm based on Particle Swarm Optimization is 94.17%.

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