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Journal articles on the topic 'Adapted naive bayes algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Alhussan, Amel, and Khalil El Hindi. "Selectively Fine-Tuning Bayesian Network Learning Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (2016): 1651005. http://dx.doi.org/10.1142/s0218001416510058.

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In this work, we propose a Selective Fine-Tuning algorithm for Bayesian Networks (SFTBN). The aim is to enhance the accuracy of Bayesian Network (BN) classifiers by finding better estimations for the probability terms used by the classifiers. The algorithm augments a BN learning algorithm with a fine-tuning stage that aims to more accurately estimate the probability terms used by the BN. If the value of a probability term causes a misclassification of a training instances and falls outside its valid range then we update (fine-tune) that value. The amount of such an update is proportional to th

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Shao, Wenjuan, Qingguo Shen, Xianli Jin, Liaoruo Huang, and Jingjing Chen. "Nonuniform Granularity-Based Classification in Social Interest Detection." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/5054825.

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Social interest detection is a new computing paradigm which processes a great variety of large scale resources. Effective classification of these resources is necessary for the social interest detection. In this paper, we describe some concepts and principles about classification and present a novel classification algorithm based on nonuniform granularity. Clustering algorithm is used to generate a clustering pedigree chart. By using suitable classification cutting values to cut the chart, we can get different branches which are used as categories. The size of cutting value is vital to the per

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Heickal, Hasnain, Tao Zhang, and Md Hasanuzzaman. "Computer Vision-Based Real-Time 3D Gesture Recognition Using Depth Image." International Journal of Image and Graphics 15, no. 01 (2015): 1550004. http://dx.doi.org/10.1142/s0219467815500047.

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Gesture is one of the fundamental ways of human machine natural interaction. To understand gesture, the system should be able to interpret 3D movements of human. This paper presents a computer vision-based real-time 3D gesture recognition system using depth image which tracks 3D joint position of head, neck, shoulder, arms, hands and legs. This tracking is done by Kinect motion sensor with OpenNI API and 3D motion gesture is recognized using the movement trajectory of those joints. User to Kinect sensor distance is adapted using proposed center of gravity (COG) correction method and 3D joint p

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Rodrigo, Enrique G., Juan C. Alfaro, Juan A. Aledo, and José A. Gámez. "Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem." Entropy 23, no. 4 (2021): 420. http://dx.doi.org/10.3390/e23040420.

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The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permuta

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T, GopalaKrishnan, and P. Sengottuvelan. "A hybrid PSO with Naïve Bayes classifier for disengagement detection in online learning." Program 50, no. 2 (2016): 215–24. http://dx.doi.org/10.1108/prog-07-2015-0047.

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Purpose – The ultimate objective of the any e-Learning system is to meet the specific need of the online learners and provide them with various features to have efficacious learning experiences by understanding their complexities. Any e-Learning system could be much more improved by tracking students commitment and disengagement on that course, in turn, would allow system to have personalized involvements at appropriate times in order to re-engage learners. Motivations play a important role to get back the learners on the track could be done by analyzing of several attributes of the log files.

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Lo Duca, Angelica, and Andrea Marchetti. "Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta." Journal of Systems and Information Technology 12, no. 3 (2020): 289–307. http://dx.doi.org/10.1108/jsit-10-2019-0212.

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Purpose Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP. Design/methodology/approach Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be don

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Mauldin, Taylor, Marc Canby, Vangelis Metsis, Anne Ngu, and Coralys Rivera. "SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning." Sensors 18, no. 10 (2018): 3363. http://dx.doi.org/10.3390/s18103363.

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This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall de

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LIANG, HAN, YUHONG YAN, and HARRY ZHANG. "LEARNING DECISION TREES WITH LOG CONDITIONAL LIKELIHOOD." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 01 (2010): 117–51. http://dx.doi.org/10.1142/s0218001410007877.

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In machine learning and data mining, traditional learning models aim for high classification accuracy. However, accurate class probability prediction is more desirable than classification accuracy in many practical applications, such as medical diagnosis. Although it is known that decision trees can be adapted to be class probability estimators in a variety of approaches, and the resulting models are uniformly called Probability Estimation Trees (PETs), the performances of these PETs in class probability estimation, have not yet been investigated. We begin our research by empirically studying

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Al-Tarawneh, Ahmed, and Ja’afer Al-Saraireh. "Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 12321–37. http://dx.doi.org/10.3233/jifs-210458.

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Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks techni

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Alkasem, Ameen, Hongwei Liu, Muhammad Shafiq, and Decheng Zuo. "A New Theoretical Approach: A Model Construct for Fault Troubleshooting in Cloud Computing." Mobile Information Systems 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/9038634.

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In cloud computing, there are four effective measurement criteria: (I) priority, (II) fault probability, (III) risk, and (IV) the duration of the repair action determining the efficacy of troubleshooting. In this paper, we propose a new theoretical algorithm to construct a model for fault troubleshooting; we do this by combining a Naïve-Bayes classifier (NBC) with a multivalued decision diagram (MDD) and influence diagram (ID), which structure and manage problems related to unambiguous modeling for any connection between significant entities. First, the NBC establish the fault probability base

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Utami, Dwi Yuni, Elah Nurlelah, and Noer Hikmah. "Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (2020): 76–85. http://dx.doi.org/10.31289/jite.v4i1.3793.

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Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm h

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JIANG, LIANGXIAO, DIANHONG WANG, and ZHIHUA CAI. "DISCRIMINATIVELY WEIGHTED NAIVE BAYES AND ITS APPLICATION IN TEXT CLASSIFICATION." International Journal on Artificial Intelligence Tools 21, no. 01 (2012): 1250007. http://dx.doi.org/10.1142/s0218213011004770.

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Many approaches are proposed to improve naive Bayes by weakening its conditional independence assumption. In this paper, we work on the approach of instance weighting and propose an improved naive Bayes algorithm by discriminative instance weighting. We called it Discriminatively Weighted Naive Bayes. In each iteration of it, different training instances are discriminatively assigned different weights according to the estimated conditional probability loss. The experimental results based on a large number of UCI data sets validate its effectiveness in terms of the classification accuracy and A

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Gouthami, Shiramshetty. "Ranking Popular Items By Naive Bayes Algorithm." International Journal of Computer Science and Information Technology 4, no. 1 (2012): 147–63. http://dx.doi.org/10.5121/ijcsit.2012.4112.

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Li, Jingmei, Weifei Wu, and Di Xue. "Transfer Naive Bayes algorithm with group probabilities." Applied Intelligence 50, no. 1 (2019): 61–73. http://dx.doi.org/10.1007/s10489-019-01512-6.

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Ma, Manfu, Wei Deng, Hongtong Liu, and Xinmiao Yun. "An Intrusion Detection Model based on Hybrid Classification algorithm." MATEC Web of Conferences 246 (2018): 03027. http://dx.doi.org/10.1051/matecconf/201824603027.

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Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest

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Saptono, Ristu, Meianto Eko Sulistyo, and Nur Shobriana Trihabsari. "TEXT CLASSIFICATION USING NAIVE BAYES UPDATEABLE ALGORITHM IN SBMPTN TEST QUESTIONS." Telematika 13, no. 2 (2017): 123. http://dx.doi.org/10.31315/telematika.v13i2.1728.

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Document classification is a growing interest in the research of text mining. Classification can be done based on the topics, languages, and so on. This study was conducted to determine how Naive Bayes Updateable performs in classifying the SBMPTN exam questions based on its theme. Increment model of one classification algorithm often used in text classification Naive Bayes classifier has the ability to learn from new data introduces with the system even after the classifier has been produced with the existing data. Naive Bayes Classifier classifies the exam questions based on the theme of the

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Wan, Xin Wang, and Juan Liang. "Speaker Localization in Reverberant Noisy Environment Using Principal Eigenvector and Classifier." Applied Mechanics and Materials 433-435 (October 2013): 416–19. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.416.

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Sound source localization is essential in many microphone arrays application, ranging from speech enhancement to human-computer interface. The steered response power (SRP) using the phase transform (SRP-PHAT) method has been proved robust, but the algorithm may fail to locate the sound source in highly reverberant noisy environment. The Naive-Bayes localization algorithm based on classification of cross-correlation functions outperforms the SRP-PHAT in highly reverberant noisy environment. This paper proposes the improved Naive-Bayes localization algorithm using principal eigenvector. Simulati

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Yusuf, Bustami, Muthmainna Qalbi, Basrul Basrul, Ima Dwitawati, Malahayati Malahayati, and Mega Ellyadi. "IMPLEMENTASI ALGORITMA NAIVE BAYES DAN RANDOM FOREST DALAM MEMPREDIKSI PRESTASI AKADEMIK MAHASISWA UNIVERSITAS ISLAM NEGERI AR-RANIRY BANDA ACEH." Cyberspace: Jurnal Pendidikan Teknologi Informasi 4, no. 1 (2020): 50. http://dx.doi.org/10.22373/cj.v4i1.7247.

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Academic achievement is determined by two factors, namely internal factors originating from within the individual in this case students and external factors that come from outside the individual or things that are influenced by the environment. There are many ways to find an academic achievement, one of which uses data mining which aims to predict or classify data using a classification algorithm. This study aims to 1) find out how to apply the Naive Bayes algorithm to student achievement, and 2) see the accuracy of the Naive Bayes algorithm to student achievement. This type of research is sec

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Isnain, Auliya Rahman, Nurman Satya Marga, and Debby Alita. "Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 1 (2021): 55. http://dx.doi.org/10.22146/ijccs.60718.

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The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring th

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Shu, Liang, Haigen Zhang, Yingmin You, Yonghao Cui, and Wei Chen. "Towards Fire Prediction Accuracy Enhancements by Leveraging an Improved Naïve Bayes Algorithm." Symmetry 13, no. 4 (2021): 530. http://dx.doi.org/10.3390/sym13040530.

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To improve fire prediction accuracy over existing methods, a double weighted naive Bayes with compensation coefficient (DWCNB) method is proposed for fire prediction purposes. The fire characteristic attributes and attribute values are all weighted to weaken the assumption that the naive Bayes attributes are independent and equally important. A compensation coefficient was used to compensate for the prior probability, and a five-level orthogonal testing method was employed to properly design the coefficient. The proposed model was trained with data collected from the National Institute of Stan

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Taha, Ahmed Majid, Aida Mustapha, and Soong-Der Chen. "Naive Bayes-Guided Bat Algorithm for Feature Selection." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/325973.

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When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other we

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Shedriko, Shedriko. "NAIVE BAYES ALGORITHM IN PREDICT GRADUATION OF STUDENTS." ZERO: Jurnal Sains, Matematika dan Terapan 3, no. 1 (2020): 45. http://dx.doi.org/10.30829/zero.v3i1.7665.

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<p><strong><em>Abstract.</em></strong><em> </em><em>The University of XYZ is a well established university which has five faculties with one of them is post graduate. Charging with a very low cost of tuition fee has attracted so many high school graduation and make it becoming the university with the bulk number of students. It has caused one subject is taught by more than one lecturer. This research is using quantitative analysis method with Naive Bayes Algorithm methodology in passing decision on PTI (Pengantar Teknologi Informasi) subject. The

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Dinesh, T. "Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1084–96. http://dx.doi.org/10.47059/revistageintec.v11i2.1738.

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Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the

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Wati, Risa. "PENERAPAN ALGORITMA NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI BERITA HOAX PADA MEDIA SOSIAL." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 5, no. 2 (2020): 159–64. http://dx.doi.org/10.33480/jitk.v5i2.1034.

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Social media is the most effective way to facilitate fast information, unfortunately, there are some elements who use social media to add hoax or deception to give misleading opinions to the public. Therefore a method is needed to classify hoax news and non-hoax news on social media. Naive Bayes is a simple classification algorithm but has high qualifications, but Naive Bayes has a very sensitive shortcoming in the selection of features and therefore the Particle Swarm Optimization method is needed to improve the expected results. After conducting research with the Naive Bayes method and the N

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Wibowo, Gentur Wahyu Nyipto. "PREDIKSI KELANJUTAN STUDI SISWA KE PERGURUAN TINGGI DENGAN NAIVE BAYES." Jurnal DISPROTEK 11, no. 1 (2020): 41–46. http://dx.doi.org/10.34001/jdpt.v11i1.1159.

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The need for an analysis of the predictions of continuation of student studies to college is a strong reason for this research. Because by knowing the number of students at a school who continue or not continue studies to universities become a reference to improve education services at the school concerned. Naive Bayes is an effective and efficient classification algorithm for data mining and machine learning. So in the research proposed Naive Bayes for predictions of continuation of student studies to college with k-fold validation and confusion matrix. And results from the algorithm Naive Ba

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Supriyatna, Adi, and Wida Prima Mustika. "Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil." J-SAKTI (Jurnal Sains Komputer dan Informatika) 2, no. 2 (2018): 152. http://dx.doi.org/10.30645/j-sakti.v2i2.78.

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Warts is a skin health problem that is generally characterized by the appearance of small, rough-textured lumps on the skin surface caused by a virus that is human papilloma virus (HPV). One technique of treatment of wart disease is immunotherapy, this method is a treatment by boosting the immune system to overcome the disease of warts. Naive bayes and Support Vector Machine (SVM) is a method of data mining algorithm used to classify. The aim of this study was to compare the Naive bayes algorithm with Support Vector Machine (SVM) in predicting the success of immunotherapy treatment method in t

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Putra, Yupi Kuspandi, Fathurrahman, and Muhamad Sadali. "Comparison Of Pso-Based Naive Bayes And Naive Bayes Algorithm In Determining The Feasibility Of Bumdes Credit." Journal of Physics: Conference Series 1539 (May 2020): 012030. http://dx.doi.org/10.1088/1742-6596/1539/1/012030.

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Suyadi, Suyadi, Arief Setyanto, and Hanif Al Fattah. "Analisis Perbandingan Algoritma Decision Tree (C4.5) Dan K-Naive Bayes Untuk Mengklasifikasi Penerimaan Mahasiswa Baru Tingkat Universitas." Indonesian Journal of Applied Informatics 2, no. 1 (2017): 59. http://dx.doi.org/10.20961/ijai.v2i1.13258.

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<em>Profile of PMB (New Student Admissions) students from several periods have abundant data that can be used for research. The data is in the form of student information from the majors of origin, NEM and majors now. Classifying the PMB profile data of students at the University level in Yogyakarta can know the majority of learners. Comparing some algorithms is needed to find out the best algorithm. Classification is a grouping algorithm that has several algorithms such as Decision Tree (C4.5) and K-Naive Bayes. Decision Tree (C4.5) is an algorithm with decision tree, while K-Naive Baye

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Sumpena, Sumpena, Yuma Akbar, Nirat Nirat, and Mario Hengky. "ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm." SinkrOn 4, no. 1 (2019): 88. http://dx.doi.org/10.33395/sinkron.v4i1.10150.

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Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to dete

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Santiko, Irfan, and Ikhsan Honggo. "Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease." IJIIS: International Journal of Informatics and Information Systems 2, no. 2 (2019): 56–60. http://dx.doi.org/10.47738/ijiis.v2i2.14.

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Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to det

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Parihah, Nur Isnaini, Sari Hartini, and Juarni Siregar. "Prediksi Angka Kelahiran Bayi Pada Desa Tridaya Sakti Dengan Menggunakan Algoritma Naive Bayes." Journal of Students‘ Research in Computer Science 1, no. 2 (2020): 77–88. http://dx.doi.org/10.31599/jsrcs.v1i2.423.

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The birth rate is something that can affect the increase in population growth. Large population is a burden for development. According to Malthus's Theory which states that a large population growth is not the welfare that is obtained but rather poverty will be encountered if the population is not well controlled. The number of baby births in Tridaya Sakti Village is increasing every year. Therefore Data Mining using the Naive Bayes algorithm can help in the calculation of predicting infant birth rates in Tridaya Sakti Village. Data Mining in predicting the number of infant birth rates aims to

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Zhu, Xiao Dan, Jin Song Su, Qing Feng Wu, and Huai Lin Dong. "Naive Bayes Classification Algorithm Based on Optimized Training Data." Advanced Materials Research 490-495 (March 2012): 460–64. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.460.

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Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes classification focus on the improvement of the classification algorithm, ignoring the selection of training data which has a great effect on the performance of classifier. And so a method is proposed to optimize the selection of training data in this paper. Adopting this method, the noisy instances in training data are eliminated by user-defined effectiveness threshold, improving the performance of classifier. Experimental results on large-scale data show that our ap

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Choi, Jiwon, Seoungjae Cho, Phuong Chu, Hoang Vu, Kyhyun Um, and Kyungeun Cho. "Automated Space Classification for Network Robots in Ubiquitous Environments." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/954920.

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Network robots provide services to users in smart spaces while being connected to ubiquitous instruments through wireless networks in ubiquitous environments. For more effective behavior planning of network robots, it is necessary to reduce the state space by recognizing a smart space as a set of spaces. This paper proposes a space classification algorithm based on automatic graph generation and naive Bayes classification. The proposed algorithm first filters spaces in order of priority using automatically generated graphs, thereby minimizing the number of tasks that need to be predefined by a

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Dodu, A. Y. Erwin, Deny Wiria Nugraha, and Mohammad Azhar Ayyub. "Penerapan Data Mining Untuk Mendeteksi Tingkatan Stadium Penyakit Human Immunodeficiency Virus / Acquired Immune Deficiency Syndrome (Hiv/Aids) Menggunakan Algoritma Naive Bayes Classifier (Studi Kasus Pada Rumah Sakit Umum Daerah (Rsud) Anutapura Palu)." ScientiCO : Computer Science and Informatics Journal 1, no. 1 (2019): 33. http://dx.doi.org/10.22487/j26204118.2018.v1.i1.11900.

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At Anutapura Palu General Hospital (Hospital) Anutapura Palu to perform stadium detection still using Ms.Excel application with the result of detection is still manually input, to overcome the problem the author aims to make data mining applications to detect the stage of HIV / AIDS by applying Naive Bayes algorithm with output / output result of stadium 1, stadium 2, stadium 3 and stadium 4. The research data was obtained at Anutapura Palu General Hospital (RSU) from 305 HIV / AIDS patients. Existing data were analyzed using Naive Bayes algorithm and using 11 features. Naive Bayes aims to cla

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Nanda, Yoga Aditama Ika, and Bety Wulan Sari. "NAIVE BAYES ALGORITHM IMPLEMENTATION TO DETECT HUMAN PERSONALITY DISORDERS." Jurnal Techno Nusa Mandiri 17, no. 1 (2020): 9–16. http://dx.doi.org/10.33480/techno.v17i1.1239.

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We live in a society that still sees problems regarding one's soul and personality as taboo, even though mental health is as important as physical health. A personality disorder itself is a disorder that can be seen from behavior, mindset, and attitude, which brings difficulties to life. Based on this problem, this study applies the method of Naive Bayes classifier as early detection of human personality disorders. Using a data set of 130 correspondences from the AMIKOM university scope with the age limit of 18-25 years and identified personality disorders is a borderline type disorder. The da

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Kang, Sunggwan, Bongkyung Kwon, Cheolwoo Kwon, Sangmin Park, and Ilsoo Yun. "Development of Incident Detection Algorithm Using Naive Bayes Classification." Journal of The Korea Institute of Intelligent Transport Systems 17, no. 6 (2018): 25–39. http://dx.doi.org/10.12815/kits.2018.17.6.25.

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Singh, Jagmeet. "Analysis on Hinglish Opinion Using Multinomial Naive Bayes Algorithm." IOSR Journal of Computer Engineering 02, no. 02 (2016): 73–82. http://dx.doi.org/10.9790/0661-15010020273-82.

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Yun-Fu Liu, Jing-Ming Guo, and Jiann-Der Lee. "Halftone Image Classification Using LMS Algorithm and Naive Bayes." IEEE Transactions on Image Processing 20, no. 10 (2011): 2837–47. http://dx.doi.org/10.1109/tip.2011.2136354.

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Madhu, M. S., and Dr Kirupa Ganapathy. "Detection of Liver Disorder Using RBF SVM in Comparison with Naïve Bayes to Measure the Accuracy, Precision, Sensitivity and Specificity." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 657–64. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21093.

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Aim: Machine learning techniques are rapidly used in the area of medical research due to its impressive results in diagnosis and prediction of diseases. The objective of this study is to evaluate the performance of SVM classifier in identification of liver disorder by comparing it with Naive Bayes algorithm. Methods and Materials: A total of 31619 samples are collected from three liver disease datasets available in kaggle. These samples are divided into training dataset (n = 22133 [70%]) and test dataset (n = 9486 [30%]). Accuracy, precision, specificity and sensitivity values are calculated t

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NAHAR, JESMIN, YI-PING PHOEBE CHEN, and SHAWKAT ALI. "KERNEL-BASED NAIVE BAYES CLASSIFIER FOR BREAST CANCER PREDICTION." Journal of Biological Systems 15, no. 01 (2007): 17–25. http://dx.doi.org/10.1142/s0218339007002076.

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The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found t

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M, Vishweshwaran, and . "Linear Genetic Programming, Naïve Bayes algorithm - Their Applications in Geotechnical Engineering." International Journal of Engineering & Technology 7, no. 3.12 (2018): 925. http://dx.doi.org/10.14419/ijet.v7i3.12.16562.

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Modeling soil and rock pose challenges due to uncertainties in their complex behavior. In the present study, linear genetic programming and Naïve Bayes are used in classification of liquefied and non-liquefied data. Soil and seismic parameters influencing the soil liquefaction potential are used to develop the models. Genetic Programming is the automatic creation of computer programs to perform a selected task using Darwinian natural selection. Linear genetic programming forms a peculiar subset of genetic programming where computer programs in a population are constituted as successive repetit

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Finki Dona Marleny and Mambang. "COMPARISON OF K-NN AND NAÏVE BAYES CLASSIFIER FOR ASPHYXIA FACTOR." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 3, no. 1 (2018): 13–17. http://dx.doi.org/10.20527/jtiulm.v3i1.23.

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Asphyxia is influenced by several factors, including the factors affecting the Immediate Was maternal factors That relates Conditions mother Pregnancy and childbirth such as hypoxia mother, Asphyxia factor data can be modeled using the classification approach. this paper will be compared k-nearest neighbor algorithm and Naive Bayes classifier to classify asphyxia factor. Naive Bayes uses the concept of Bayes’ Theorem which assuming the independency between predictors. Basically, Bayes theorem is used to compute the subsequent probabilities. Analysis of the two algorithms has been done on sever

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Evi Purnamasari, Dian Palupi Rini, and Sukemi. "Feature Selection using Particle Swarm Optimization Algorithm in Student Graduation Classification with Naive Bayes Method." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 3 (2020): 469–75. http://dx.doi.org/10.29207/resti.v4i3.1833.

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The study of the classification of student graduation at a university aims to help the university understand the academic development of students and to be able to find solutions in improving the development of student graduation in a timely manner. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. The classification accuracy can be improved by selecting the appropriate features. Particle Swarm Optimization is an evolutionary optimization method that can be used in feature selection to produce a better level of accuracy. The tes

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Ji, Jie, and Qiangfu Zhao. "Applying Naive Bayes Classifier to Document Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 6 (2010): 624–30. http://dx.doi.org/10.20965/jaciii.2010.p0624.

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Document clustering partitions sets of unlabeled documents so that documents in clusters share common concepts. A Naive Bayes Classifier (BC) is a simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions. BC requires a small amount of training data to estimate parameters required for classification. Since training data must be labeled, we propose an Iterative Bayes Clustering (IBC) algorithm. To improve IBC performance, we propose combining IBC with Comparative Advantage-based (CA) initialization method. Experimental results show that our pr

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Hartatik, Hartatik. "Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes." Indonesian Journal of Applied Informatics 5, no. 1 (2021): 32. http://dx.doi.org/10.20961/ijai.v5i1.44379.

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<p>Abstrak :</p><p>Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma <em>naive bayes</em>. Algoritma <em>Naive bayes</em> merupakan salah satu metode yang d

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Nugroho, Agung, and Yoga Religia. "Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (2021): 504–10. http://dx.doi.org/10.29207/resti.v5i3.3067.

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The increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Data mining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve

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Azhari, Mulkan, Zakaria Situmorang, and Rika Rosnelly. "Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 2 (2021): 640. http://dx.doi.org/10.30865/mib.v5i2.2937.

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In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest a

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Winarti, Titin, Henny Indriyawati, Vensy Vydia, and Febrian Wahyu Christanto. "Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 452. http://dx.doi.org/10.11591/ijai.v10.i2.pp452-457.

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<span id="docs-internal-guid-210930a7-7fff-b7fb-428b-3176d3549972"><span>The match between the contents of the article and the article theme is the main factor whether or not an article is accepted. Many people are still confused to determine the theme of the article appropriate to the article they have. For that reason, we need a document classification algorithm that can group the articles automatically and accurately. Many classification algorithms can be used. The algorithm used in this study is naive bayes and the k-nearest neighbor algorithm is used as the baseline. The naive

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Safri, Yofi Firdan, Riza Arifudin, and Much Aziz Muslim. "K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor." Scientific Journal of Informatics 5, no. 1 (2018): 18. http://dx.doi.org/10.15294/sji.v5i1.12057.

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Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of

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Cinarer, Gokalp, and Bulent Gursel Emiroglu. "Classification of brain tumours using radiomic features on MRI." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 80–90. http://dx.doi.org/10.18844/gjpaas.v0i12.4989.

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Glioma is one of the most common brain tumours among the diagnoses of existing brain tumours. Glioma grades are important factors that should be known in the treatment of brain tumours. In this study, the radiomic features of gliomas were analysed and glioma grades were classified by Gaussian Naive Bayes algorithm. Glioma tumours of 121 patients of Grade II and Grade III were examined. The glioma tumours were segmented with the Grow Cut Algorithm and the 3D feature of tumour magnetic resonance imaging images were obtained with the 3D Slicer programme. The obtained quantitative values were stat

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