Relevant bibliographies by topics / Adapted naive bayes algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Adapted naive bayes algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Adapted naive bayes algorithm"

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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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Dissertations / Theses on the topic "Adapted naive bayes algorithm"

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Cheeti, Srilaxmi. "Cross-domain sentiment classification using grams derived from syntax trees and an adapted naive Bayes approach." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/13733.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>There is an increasing amount of user-generated information in online documents, includ- ing user opinions on various topics and products such as movies, DVDs, kitchen appliances, etc. To make use of such opinions, it is useful to identify the polarity of the opinion, in other words, to perform sentiment classification. The goal of sentiment classification is to classify a given text/document as either positive, negative or neutral based on the words present in the document. Supervised learning approaches
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Schmidt, Samuel. "A Massively Parallel Algorithm for Cell Classification Using CUDA." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448873851.

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Agarwal, Akrita. "Exploring the Noise Resilience of Combined Sturges Algorithm." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447070335.

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Sandberg, Sebastian. "Identifying Hateful Text on Social Media with Machine Learning Classifiers and Normalization Methods - Using Support Vector Machines and Naive Bayes Algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155353.

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Hateful content on social media is a growing problem. In this thesis, machine learning algorithms and pre-processing methods have been combined in order to train classifiers in identifying hateful text on social media. The combinations have been compared in terms of performance, where the considered performance criteria have been F-score and accuracy in classification. Training are performed using Naive Bayes algorithm(NB) and Support Vector Machines (SVM). The pre-processing techniques that have been used are tokenization and normalization. Fortokenization, an open-source unigram tokenizer ha
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Ramos, Gustavo da Mota. "Seleção entre estratégias de geração automática de dados de teste por meio de métricas estáticas de softwares orientados a objetos." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-05122018-202315/.

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Produtos de software com diferentes complexidades são criados diariamente através da elicitação de demandas complexas e variadas juntamente a prazos restritos. Enquanto estes surgem, altos níveis de qualidade são esperados para tais, ou seja, enquanto os produtos tornam-se mais complexos, o nível de qualidade pode não ser aceitável enquanto o tempo hábil para testes não acompanha a complexidade. Desta maneira, o teste de software e a geração automática de dados de testes surgem com o intuito de entregar produtos contendo altos níveis de qualidade mediante baixos custos e rápidas atividades de
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Lee, Jun won. "Relationships Among Learning Algorithms and Tasks." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2478.

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Metalearning aims to obtain knowledge of the relationship between the mechanism of learning and the concrete contexts in which that mechanisms is applicable. As new mechanisms of learning are continually added to the pool of learning algorithms, the chances of encountering behavior similarity among algorithms are increased. Understanding the relationships among algorithms and the interactions between algorithms and tasks help to narrow down the space of algorithms to search for a given learning task. In addition, this process helps to disclose factors contributing to the similar behavior of di
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Petřík, Patrik. "Predikce vývoje akciového trhu prostřednictvím technické a psychologické analýzy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222507.

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This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.
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Maršál, Martin. "Elektronický modul pro akustickou detekci." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-240831.

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This diploma thesis deals with the design and implementation of an electronic module for acoustic detection. The module has the task of detecting a predetermined acoustic signals through them learned classification model. The module is used mainly for security purposes. To identify and classify the proposed model using machine learning techniques. Given the possibility of retraining for a different set of sounds, the module becomes a universal sound detector. With acoustic sound using the digital MEMS microphone, for which it is designed and implemented conversion filter. The resulting system
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Sarr, Ndey Binta, and 莎妮塔. "Hybrid of Filter Wrapper using Naive Bayes Algorithm and Genetic Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nqhgvw.

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碩士<br>元智大學<br>生物與醫學資訊碩士學位學程<br>106<br>Feature selection is an essential data preprocessing method and has been generally studied in data mining and machine learning. In this paper, we presented an effective feature selection approach using the hybrid method. That is using the filter method to select the most informative features from the dataset, then we used the wrapper method with a genetic search to select relevance features and to remove the redundancy of the features in the dataset. We finally run those features with the combination of the two algorithm, Naïve Bayes and Genetic Algorithm
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Chang, Liang-Hao, and 張良豪. "Improving the performance of Naive Bayes Classifier by using Selective Naive Bayesian Algorithm and Prior Distributions." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/92613736217287175606.

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碩士<br>國立成功大學<br>工業與資訊管理學系碩博士班<br>97<br>Naive Bayes classifiers have been widely used for data classification because of its computational efficiency and competitive accuracy. When all attributes are employed for classification, the accuracy of the naive Bayes classifier is generally affected by noisy attributes. A mechanism for attribute selection should be considered for improving its prediction accuracy. Selective naive Bayesian method is a very successful approach for removing noisy and/or redundant attributes. In addition, attributes are generally assumed to have prior distributions, such
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Book chapters on the topic "Adapted naive bayes algorithm"

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Zhang, Huajie, and Charles X. Ling. "An Improved Learning Algorithm for Augmented Naive Bayes." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45357-1_62.

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Josephine Theresa, S., and D. J. Evangeline. "Classification of Diabetes Milletus Using Naive Bayes Algorithm." In Intelligence in Big Data Technologies—Beyond the Hype. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5285-4_40.

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Rodríguez, Jorge Enrique Rodríguez, Víctor Hugo Medina García, and Nelson Pérez Castillo. "Webpages Classification with Phishing Content Using Naive Bayes Algorithm." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21451-7_21.

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Xu, Mao, and Haiyan Huang. "Design of Class Management System Based on Naive Bayes Algorithm." In Application of Intelligent Systems in Multi-modal Information Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51556-0_119.

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Kewat, Anil, P. N. Srivastava, and Arvind Kumar Sharma. "Performance Analysis of Naive Bayes Computing Algorithm for Blood Donors Classification Problem." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2372-0_43.

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Zhang, Huanying, Yushui Geng, and Fei Wang. "An Improved Attribute Value-Weighted Double-Layer Hidden Naive Bayes Classification Algorithm." In Proceedings of the 9th International Conference on Computer Engineering and Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3753-0_31.

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Aiguzhinov, Artur, Carlos Soares, and Ana Paula Serra. "A Similarity-Based Adaptation of Naive Bayes for Label Ranking: Application to the Metalearning Problem of Algorithm Recommendation." In Discovery Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16184-1_2.

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Singh, Law Kumar, Pooja, Hitendra Garg, Munish Khanna, and Robin Singh Bhadoria. "An Analytical Study on Machine Learning Techniques." In Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5876-8.ch007.

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The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.
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Winarti, Titin, and Vensy Vydia. "Feature selection for optimizing the Naive Bayes algorithm." In Engineering, Information and Agricultural Technology in the Global Digital Revolution. CRC Press, 2020. http://dx.doi.org/10.1201/9780429322235-10.

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Solanki, Arun, and Rajat Saxena. "Text Classification Using Self-Structure Extended Multinomial Naive Bayes." In Handbook of Research on Emerging Trends and Applications of Machine Learning. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9643-1.ch006.

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With the advent of neural networks and its subfields like deep neural networks and convolutional neural networks, it is possible to make text classification predictions with high accuracy. Among the many subtypes of naive Bayes, multinomial naive Bayes is used for text classification. Many attempts have been made to somehow develop an algorithm that uses the simplicity of multinomial naive Bayes and at the same time incorporates feature dependency. One such effort was put in structure extended multinomial naive Bayes, which uses one-dependence estimators to inculcate dependencies. Basically, one-dependence estimators take one of the attributes as features and all other attributes as its child. This chapter proposes self structure extended multinomial naïve Bayes, which presents a hybrid model, a combination of the multinomial naive Bayes and structure extended multinomial naive Bayes. Basically, it tries to classify the instances that were misclassified by structure extended multinomial naive Bayes as there was no direct dependency between attributes.
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Conference papers on the topic "Adapted naive bayes algorithm"

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Kamel, Hajer, Dhahir Abdulah, and Jamal M. Al-Tuwaijari. "Cancer Classification Using Gaussian Naive Bayes Algorithm." In 2019 International Engineering Conference (IEC). IEEE, 2019. http://dx.doi.org/10.1109/iec47844.2019.8950650.

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Sharmila, B. S., and Rohini Nagapadma. "Intrusion Detection System using Naive Bayes algorithm." In 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2019. http://dx.doi.org/10.1109/wiecon-ece48653.2019.9019921.

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Hartatik, Kusrini Kusrini, and Agung Budi Prasetio. "Prediction of Student Graduation with Naive Bayes Algorithm." In 2020 Fifth International Conference on Informatics and Computing (ICIC). IEEE, 2020. http://dx.doi.org/10.1109/icic50835.2020.9288625.

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Sathyadevan, Shiju, P. R. Sarath, U. Athira, and V. Anjana. "Improved document classification through enhanced Naive Bayes algorithm." In 2014 International Conference on Data Science & Engineering (ICDSE). IEEE, 2014. http://dx.doi.org/10.1109/icdse.2014.6974619.

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Malik, Reza Firsandaya, Eko Pratama, Huda Ubaya, Rido Zulfahmi, Deris Stiawan, and Kemahyanto Exaudi. "Object Position Estimation Using Naive Bayes Classifier Algorithm." In 2018 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2018. http://dx.doi.org/10.1109/icecos.2018.8605198.

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Widiyaningtyas, Triyanna, Ilham Ari Elbaith Zaeni, and Nadiratin Jamilah. "Diagnosis of fever symptoms using naive bayes algorithm." In SIET '20: 5th International Conference on Sustainable Information Engineering and Technology. ACM, 2020. http://dx.doi.org/10.1145/3427423.3427426.

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K, Agarshana, Karan P, Leo Celestine S, and Vasantha V Kumar. "Naive Bayes Algorithm for Sentiment Analysis on Twitter." In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2021. http://dx.doi.org/10.1109/icscan53069.2021.9526473.

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Goutam, Siddharth, and Srija Unnikrishnan. "Decision for Vertical Handover based on Naive Bayes Algorithm." In 2019 International Conference on Advances in Computing, Communication and Control (ICAC3). IEEE, 2019. http://dx.doi.org/10.1109/icac347590.2019.9036820.

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Tzanos, Georgios, Christoforos Kachris, and Dimitrios Soudris. "Hardware Acceleration on Gaussian Naive Bayes Machine Learning Algorithm." In 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2019. http://dx.doi.org/10.1109/mocast.2019.8741875.

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He, D. P., Z. L. He, and C. Liu. "Recommendation Algorithm Combining Tag Data and Naive Bayes Classification." In 2020 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME). IEEE, 2020. http://dx.doi.org/10.1109/icedme50972.2020.00156.

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