Relevant bibliographies by topics / Bayes Algorithm

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Academic literature on the topic 'Bayes Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bayes Algorithm"

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Wu, Qinghua, Bin Wu, Chengyu Hu, and Xuesong Yan. "Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm." Symmetry 13, no. 2 (February 16, 2021): 322. http://dx.doi.org/10.3390/sym13020322.

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As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm’s shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance.
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Zonyfar, Candra. "Student Enrollment: Data Mining Using Naïve Bayes Algorithm." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1077–83. http://dx.doi.org/10.5373/jardcs/v12sp7/20202205.

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M, Harshitha, and Dr B. M. Sagar. "Smart Health Care Implementation Using Naïve Bayes Algorithm." International Journal of Innovative Research in Computer Science & Technology 7, no. 3 (May 2019): 90–93. http://dx.doi.org/10.21276/ijircst.2019.7.3.11.

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Noviriandini, Astrid, and Nurajijah Nurajijah. "ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRESTASI SISWA SEKOLAH MENENGAH KEJURUAN." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 5, no. 1 (August 7, 2019): 23–28. http://dx.doi.org/10.33480/jitk.v5i1.607.

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This research informs students and teachers to anticipate early in following the learning period in order to get maximum learning outcomes. The method used is C4.5 decision tree algorithm and Naïve Bayes algorithm. The purpose of this study was to compare and evaluate the decision tree model C4.5 as the selected algorithm and Naïve Bayes to find out algorithms that have higher accuracy in predicting student achievement. Learning achievement can be measured by the value of report cards. After comparison of the two algorithms, the results of the learning achievement prediction are obtained. The results showed that the Naïve Bayes algorithm had an accuracy value of 95.67% and the AUC value of 0.999 was included in Excellent Clasification, for the C4.5 algorithm the accuracy value was 90.91% and the AUC value of 0.639 was included in the state of Poor Clasification. Thus the Naïve Bayes algorithm can better predict student achievement.
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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 (June 5, 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 experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.
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Pizzo, Anaïs, Pascal Teyssere, and Long Vu-Hoang. "Boosted Gaussian Bayes Classifier and its application in bank credit scoring." Journal of Advanced Engineering and Computation 2, no. 2 (June 30, 2018): 131. http://dx.doi.org/10.25073/jaec.201822.193.

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With the explosion of computer science in the last decade, data banks and networksmanagement present a huge part of tomorrows problems. One of them is the development of the best classication method possible in order to exploit the data bases. In classication problems, a representative successful method of the probabilistic model is a Naïve Bayes classier. However, the Naïve Bayes effectiveness still needs to be upgraded. Indeed, Naïve Bayes ignores misclassied instances instead of using it to become an adaptive algorithm. Different works have presented solutions on using Boosting to improve the Gaussian Naïve Bayes algorithm by combining Naïve Bayes classier and Adaboost methods. But despite these works, the Boosted Gaussian Naïve Bayes algorithm is still neglected in the resolution of classication problems. One of the reasons could be the complexity of the implementation of the algorithm compared to a standard Gaussian Naïve Bayes. We present in this paper, one approach of a suitable solution with a pseudo-algorithm that uses Boosting and Gaussian Naïve Bayes principles having the lowest possible complexity. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 (July 20, 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 has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.
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M. V., Ishwarya, and K. Ramesh Kumar. "Selective Colligation and Selective Scrambling for Privacy Preservation in Data Mining." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 778. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp778-785.

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The work is to enhance the time efficiency in retrieving the data from enormous bank database. The major drawback in retrieving data from large databases is time delay. This time hindrance is owed as the already existing method (SVM), Abstract Data Type (ADT) tree pursues some elongated Sequential steps. These techniques takes additional size and with a reduction of speed in training and testing. Another major negative aspect of these techniques is its Algorithmic complexity. The classification algorithms have five categories. They are ID3, k-nearest neighbour, Decision tree, ANN, and Naïve Bayes algorithm. To triumph over the drawbacks in SVM techniques, we worn a technique called Naïve Bayes Classification (NBC) Algorithm that works in parallel manner rather than sequential manner. For further enhancement we commenced a Naïve Bayes updatable algorithm which is the advanced version of Naïve Bayes classification algorithm. Thus the proposed technique Naïve bayes algorithm ensures that miner can mine more efficiently from the enormous database.
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Lasulika, Mohamad Efendi. "KOMPARASI NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR UNTUK MENGETAHUI AKURASI TERTINGGI PADA PREDIKSI KELANCARAN PEMBAYARAN TV KABEL." ILKOM Jurnal Ilmiah 11, no. 1 (May 8, 2019): 11–16. http://dx.doi.org/10.33096/ilkom.v11i1.408.11-16.

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One obstacle of the default payment is the lack of analysis in the new customer acceptance process which is only reviewed from the form provided at registration, as for the purpose of this study to find out the highest accuracy results from the comparison of Naïve Bayes, SVM and K-NN Algorithms. It can be seen that the Naïve Bayes algorithm which has the highest accuracy value is 96%, while the K-Neural Network algorithm has the highest accuracy at K = 3 which is 92%, while Support Vector Machine only gets accuracy of 66%. The ROC Curve results show that Naïve Bayes achieved the best AUC value of 0.99. Comparison between data mining classification algorithms namely Naïve Bayes, K-Neural Network and Support Vector Machine for predicting smooth payment using multivariate data types, Naïve Bayes method is an accurate algorithm and this method is also very dominant towards other methods. Based on Accuracy, AUC and T-tests this method falls into the best classification category.
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ZHANG, HARRY. "EXPLORING CONDITIONS FOR THE OPTIMALITY OF NAÏVE BAYES." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 02 (March 2005): 183–98. http://dx.doi.org/10.1142/s0218001405003983.

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Naïve Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based is rarely true in real-world applications. An open question is: what is the true reason for the surprisingly good performance of Naïve Bayes in classification? In this paper, we propose a novel explanation for the good classification performance of Naïve Bayes. We show that, essentially, dependence distribution plays a crucial role. Here dependence distribution means how the local dependence of an attribute distributes in each class, evenly or unevenly, and how the local dependences of all attributes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out). Specifically, we show that no matter how strong the dependences among attributes are, Naïve Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary condition for the optimality of Naïve Bayes. Further, we investigate the optimality of Naïve Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of Naïve Bayes, in which the dependences among attributes exist. This provides evidence that dependences may cancel each other out. Our theoretic analysis can be used in designing learning algorithms. In fact, a major class of learning algorithms for Bayesian networks are conditional independence-based (or CI-based), which are essentially based on dependence. We design a dependence distribution-based algorithm by extending the ChowLiu algorithm, a widely used CI based algorithm. Our experiments show that the new algorithm outperforms the ChowLiu algorithm, which also provides empirical evidence to support our new explanation.
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Dissertations / Theses on the topic "Bayes Algorithm"

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Bissmark, Johan, and Oscar Wärnling. "The Sparse Data Problem Within Classification Algorithms : The Effect of Sparse Data on the Naïve Bayes Algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209227.

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In today’s society, software and apps based on machine learning and predictive analysis are of the essence. Machine learning has provided us with the possibility of predicting likely future outcomes based on previously collected data in order to save time and resources.   A common problem in machine learning is sparse data, which alters the performance of machine learning algorithms and their ability to calculate accurate predictions. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis.   This report will mainly focus on the Naïve Bayes classification algorithm and how it is affected by sparse data in comparison to other widely used classification algorithms. The significance of the performance loss associated with sparse data is studied and analyzed, in order to measure the effect sparsity has on the ability to compute accurate predictions.   In conclusion, the results of this report lay a solid argument for the conclusion that the Naïve Bayes algorithm is far less affected by sparse data compared to other common classification algorithms. A conclusion that is in line with what previous research suggests.
I dagens samhälle är maskininlärningsbaserade applikationer och mjukvara, tillsammans med förutsägelser, högst aktuellt. Maskininlärning har gett oss möjligheten att förutsäga troliga utfall baserat på tidigare insamlad data och därigenom spara tid och resurser.   Ett vanligt förekommande problem inom maskininlärning är gles data, eftersom det påverkar prestationen hos algoritmer för maskininlärning och deras förmåga att kunna beräkna precisa förutsägelser. Data anses vara gles när vissa förväntade värden i ett dataset saknas, vilket generellt är vanligt förekommande i storskaliga dataset.   I den här rapporten ligger fokus huvudsakligen på klassificeringsalgoritmen Naïve Bayes och hur den påverkas av gles data jämfört med andra frekvent använda klassifikationsalgoritmer. Omfattningen av prestationssänkningen som resultat av gles data studeras och analyseras för att mäta hur stor effekt gles data har på förmågan att kunna beräkna precisa förutsägelser.   Avslutningsvis lägger resultaten i den här rapporten grund för slutsatsen att algoritmen Naïve Bayes påverkas mindre av gles data jämfört med andra vanligt förekommande klassificeringsalgoritmer. Den här rapportens slutsats stöds även av vad tidigare forskning har visat.
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Volfson, Alexander. "Exploring the optimal Transformation for Volatility." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/472.

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This paper explores the fit of a stochastic volatility model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian distribution, to the continuously compounded daily returns of the Australian stock index. Estimation was difficult, and over-fitting likely, because more variables are present than data. We developed a revised model that held a couple of these variables fixed and then, further, a model which reduced the number of variables significantly by grouping trading days. A Metropolis-Hastings algorithm was used to simulate the joint density and derive estimated volatilities. Though autocorrelations were higher with a smaller Box-Cox transformation parameter, the fit of the distribution was much better.
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Nguyen, Huu Du. "System Reliability : Inference for Common Cause Failure Model in Contexts of Missing Information." Thesis, Lorient, 2019. http://www.theses.fr/2019LORIS530.

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Le bon fonctionnement de l’ensemble d’un système industriel est parfois fortement dépendant de la fiabilité de certains éléments qui le composent. Une défaillance de l’un de ces éléments peut conduire à une défaillance totale du système avec des conséquences qui peuvent être catastrophiques en particulier dans le secteur de l’industrie nucléaire ou dans le secteur de l’industrie aéronautique. Pour réduire ce risque de panne catastrophique, une stratégie consiste à dupliquer les éléments sensibles dans le dispositif. Ainsi, si l’un de ces éléments tombe en panne, un autre pourra prendre le relais et le bon fonctionnement du système pourra être maintenu. Cependant, on observe couramment des situations qui conduisent à des défaillances simultanées d’éléments du système : on parle de défaillance de cause commune. Analyser, modéliser, prédire ce type d’événement revêt donc une importance capitale et sont l’objet des travaux présentés dans cette thèse. Il existe de nombreux modèles pour les défaillances de cause commune. Des méthodes d’inférence pour étudier les paramètres de ces modèles ont été proposées. Dans cette thèse, nous considérons la situation où l’inférence est menée sur la base de données manquantes. Nous étudions en particulier le modèle BFR (Binomial Failure Rate) et la méthode des facteurs alpha. En particulier, une approche bayésienne est développée en s’appuyant sur des techniques algorithmiques (Metropolis, IBF). Dans le domaine du nucléaire, les données de défaillances sont peu abondantes et des techniques particulières d’extrapolations de données doivent être mis en oeuvre pour augmenter l’information. Nous proposons dans le cadre de ces stratégies, des techniques de prédiction des défaillances de cause commune. L’actualité récente a mis en évidence l’importance de la fiabilité des systèmes redondants et nous espérons que nos travaux contribueront à une meilleure compréhension et prédiction des risques de catastrophes majeures
The effective operation of an entire industrial system is sometimes strongly dependent on the reliability of its components. A failure of one of these components can lead to the failure of the system with consequences that can be catastrophic, especially in the nuclear industry or in the aeronautics industry. To reduce this risk of catastrophic failures, a redundancy policy, consisting in duplicating the sensitive components in the system, is often applied. When one of these components fails, another will take over and the normal operation of the system can be maintained. However, some situations that lead to simultaneous failures of components in the system could be observed. They are called common cause failure (CCF). Analyzing, modeling, and predicting this type of failure event are therefore an important issue and are the subject of the work presented in this thesis. We investigate several methods to deal with the statistical analysis of CCF events. Different algorithms to estimate the parameters of the models and to make predictive inference based on various type of missing data are proposed. We treat confounded data using a BFR (Binomial Failure Rare) model. An EM algorithm is developed to obtain the maximum likelihood estimates (MLE) for the parameters of the model. We introduce the modified-Beta distribution to develop a Bayesian approach. The alpha-factors model is considered to analyze uncertainties in CCF. We suggest a new formalism to describe uncertainty and consider Dirichlet distributions (nested, grouped) to make a Bayesian analysis. Recording of CCF cause data leads to incomplete contingency table. For a Bayesian analysis of this type of tables, we propose an algorithm relying on inverse Bayes formula (IBF) and Metropolis-Hasting algorithm. We compare our results with those obtained with the alpha- decomposition method, a recent method proposed in the literature. Prediction of catastrophic event is addressed and mapping strategies are described to suggest upper bounds of prediction intervals with pivotal method and Bayesian techniques. Recent events have highlighted the importance of reliability redundant systems and we hope that our work will contribute to a better understanding and prediction of the risks of major CCF events
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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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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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Chao, Yang, and Peng Zhang. "One General Approach For Analysing Compositional Structure Of Terms In Biomedical Field." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Informationsteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-20913.

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The root is the primary lexical unit of Ontological terms, which carries the most significant aspects of semantic content and cannot be reduced into small constituents. It is the key of ontological term structure. After the identification of root, we can easily get the meaning of terms. According to the meaning, it’s helpful to identify the other parts of terms, such as the relation, definition and so on. We have generated a general classification model to identify the roots of terms in this master thesis. There are four features defined in our classification model: the Token, the POS, the Length and the Position. Implementation is followed using Java and algorithm is followed using Naïve Bayes. We implemented and evaluated the classification model using Gene Ontology (GO). The evaluation results showed that our framework and model were effective.
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Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.

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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.
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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 have been used while a normalization model that normalizes each tweet pre-classification have been developed in Java. Normalization include basic clean up methods such as removing stop words, URLs, and punctuation, as well as altering methods such as emoticon conversion and spell checking. Both binary and multi-class versions of the classifiers have been used on balanced and unbalanced data. Both machine learning algorithms perform on a reasonable level with accuracy between 76.70% and 93.55% and an F-score between 0.766 and 0.935. The results point towards the fact that the main purpose of normalization is to reduce noise, balancing data is necessary and that SVM seem to slightly outperform NB.
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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 teste. Porém, neste contexto, os profissionais de desenvolvimento dependem das estratégias de geração automáticas de testes e principalmente da seleção da técnica mais adequada para conseguir maior cobertura de código possível, este é um fator importante dados que cada técnica de geração de dados de teste possui particularidades e problemas que fazem seu uso melhor em determinados tipos de software. A partir desde cenário, o presente trabalho propõe a seleção da técnica adequada para cada classe de um software com base em suas características, expressas por meio de métricas de softwares orientados a objetos a partir do algoritmo de classificação Naive Bayes. Foi realizada uma revisão bibliográfica de dois algoritmos de geração, algoritmo de busca aleatório e algoritmo de busca genético, compreendendo assim suas vantagens e desvantagens tanto de implementação como de execução. As métricas CK também foram estudadas com o intuito de compreender como estas podem descrever melhor as características de uma classe. O conhecimento adquirido possibilitou coletar os dados de geração de testes de cada classe como cobertura de código e tempo de geração a partir de cada técnica e também as métricas CK, permitindo assim a análise destes dados em conjunto e por fim execução do algoritmo de classificação. Os resultados desta análise demonstraram que um conjunto reduzido e selecionado das métricas CK é mais eficiente e descreve melhor as características de uma classe se comparado ao uso do conjunto por completo. Os resultados apontam também que as métricas CK não influenciam o tempo de geração dos dados de teste, entretanto, as métricas CK demonstraram correlação moderada e influência na seleção do algoritmo genético, participando assim na sua seleção pelo algoritmo Naive Bayes
Software products with different complexity are created daily through analysis of complex and varied demands together with tight deadlines. While these arise, high levels of quality are expected for such, as products become more complex, the quality level may not be acceptable while the timing for testing does not keep up with complexity. In this way, software testing and automatic generation of test data arise in order to deliver products containing high levels of quality through low cost and rapid test activities. However, in this context, software developers depend on the strategies of automatic generation of tests and especially on the selection of the most adequate technique to obtain greater code coverage possible, this is an important factor given that each technique of data generation of test have peculiarities and problems that make its use better in certain types of software. From this scenario, the present work proposes the selection of the appropriate technique for each class of software based on its characteristics, expressed through object oriented software metrics from the naive bayes classification algorithm. Initially, a literature review of the two generation algorithms was carried out, random search algorithm and genetic search algorithm, thus understanding its advantages and disadvantages in both implementation and execution. The CK metrics have also been studied in order to understand how they can better describe the characteristics of a class. The acquired knowledge allowed to collect the generation data of tests of each class as code coverage and generation time from each technique and also the CK metrics, thus allowing the analysis of these data together and finally execution of the classification algorithm. The results of this analysis demonstrated that a reduced and selected set of metrics is more efficient and better describes the characteristics of a class besides demonstrating that the CK metrics have little or no influence on the generation time of the test data and on the random search algorithm . However, the CK metrics showed a medium correlation and influence in the selection of the genetic algorithm, thus participating in its selection by the algorithm naive bayes
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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 different algorithms. We first study general characteristics of learning tasks and their correlation with the performance of algorithms, isolating two metafeatures whose values are fairly distinguishable between easy and hard tasks. We then devise a new metafeature that measures the difficulty of a learning task that is independent of the performance of learning algorithms on it. Building on these preliminary results, we then investigate more formally how we might measure the behavior of algorithms at a ner grained level than a simple dichotomy between easy and hard tasks. We prove that, among all many possible candidates, the Classifi er Output Difference (COD) measure is the only one possessing the properties of a metric necessary for further use in our proposed behavior-based clustering of learning algorithms. Finally, we cluster 21 algorithms based on COD and show the value of the clustering in 1) highlighting interesting behavior similarity among algorithms, which leads us to a thorough comparison of Naive Bayes and Radial Basis Function Network learning, and 2) designing more accurate algorithm selection models, by predicting clusters rather than individual algorithms.
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Books on the topic "Bayes Algorithm"

1

Algorithmic algebraic combinatorics and Gröbner bases. Heidelberg: Springer, 2009.

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Klin, Mikhail, Gareth A. Jones, Aleksandar Jurišić, Mikhail Muzychuk, and Ilia Ponomarenko, eds. Algorithmic Algebraic Combinatorics and Gröbner Bases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01960-9.

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Li, Huishi. Noncommutative Gr bner Bases and Filtered-Graded Transfer. Berlin: Springer-Verlag Berlin/Heidelberg, 2002.

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Diophantine equations and power integral bases: New computational methods. Boston: Birkhäuser, 2002.

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Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.
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Kravchenko, Igor', Maksim Glinskiy, Sergey Karcev, Viktor Korneev, and Diana Abdumuminova. Resource-saving plasma technology in the repair of processing equipment. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1083289.

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In the monograph methodological bases of selection of method of coating, design of technological processes of hardening and recovery of the wearing surfaces of parts using a systems engineering analysis and information support technologist. The mathematical model of plasma spraying of materials with different thermal conductivity and methods criteria for evaluation of technical and technological opportunities of a plasma coating method. Describes the methods and results of experimental studies, the analysis of the conditions and causes of loss of efficiency of processing equipment APK. The proposed scientific and methodical approach to the justification of expediency of the recovery and strengthening of the working bodies and parts expensive imported technological equipment. The proposed mathematical model describing the physical processes in plasma coating for various applications. The structure of the algorithm for solving the task of hardening and recovery of worn parts plasma methods on the basis of the integrated CAE system. This monograph is intended for employees of scientific research institutions, specialists of machine-building production and enterprises of technical service, as well as teachers, postgraduates and students of agricultural engineering areas of training.
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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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McGlothin, Charles C. Ambient sound in the ocean induced by heavy precipitation and the subsequent predictability of rainfall rate. Monterey, California: Naval Postgraduate School, 1991.

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International Phoenix Conference on Computers and Communications (13th 1994 Phoenix, Ariz.). 1994 IEEE 13th Annual International Phoenix Conference on Computers and Communications: April 12-15, 1994, Phoenix, Arizona. Piscataway, N.J: IEEE, 1994.

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Efficient structures for geometric data management. Berlin: Springer-Verlag, 1988.

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Book chapters on the topic "Bayes Algorithm"

1

Xu, WanShan, JianBiao Zhang, and YaHao Zhang. "A Trusted Connection Authentication Reinforced by Bayes Algorithm." In Advances in Brain Inspired Cognitive Systems, 727–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00563-4_71.

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Yu, Fei, Yue Shen, Huang Huang, Cheng Xu, and Xia-peng Dai. "An Information Audit System Based on Bayes Algorithm." In Lecture Notes in Computer Science, 869–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11610496_120.

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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, 581–86. Berlin, Heidelberg: 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, 401–12. Singapore: 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, 249–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21451-7_21.

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Cui, Jianming. "Pattern Recognition of Handwritten Text Based on Bayes Algorithm." In Communications in Computer and Information Science, 442–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22456-0_63.

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Masrani, Manav, and Poornalatha G. "Twitter Sentiment Analysis Using a Modified Naïve Bayes Algorithm." In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, 171–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67220-5_16.

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Marikani, T., and K. Shyamala. "Modified Multinomial Naïve Bayes Algorithm for Heart Disease Prediction." In Intelligent Communication Technologies and Virtual Mobile Networks, 294–300. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28364-3_27.

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Liu, Xiaoming, Jianwei Yin, Jinxiang Dong, and Memon Abdul Ghafoor. "An Improved FloatBoost Algorithm for Naïve Bayes Text Classification." In Advances in Web-Age Information Management, 162–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11563952_15.

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Kim, Han-joon, and Jae-young Chang. "Improving Naïve Bayes Text Classifier with Modified EM Algorithm." In Lecture Notes in Computer Science, 326–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39592-8_45.

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Conference papers on the topic "Bayes Algorithm"

1

Jia, Shaocheng, Yun Yue, Zi Yang, Xin Pei, and Yashen Wang. "Travelling Modes Recognition via Bayes Neural Network with Bayes by Backprop Algorithm." In 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482933.343.

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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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Yi-jun, Li, Zou Peng, and Ye Qiang. "Customer Sample Difference-oriented Bayes Segmentation Algorithm." In 2006 International Conference on Management Science and Engineering. IEEE, 2006. http://dx.doi.org/10.1109/icmse.2006.313914.

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Ma, Xiaolong, Gang Liu, Bing He, Kaijie Zhang, Xianyang Zhang, and Xin Zhao. "Trajectory Prediction Algorithm Based on Variational Bayes." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9018897.

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Guodong Li and Liangjun Wen. "Intellectual information circle based on bayes algorithm." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622946.

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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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Gai, Yulian, and Yaping Wang. "Data Fusion and Bayes Estimation Algorithm Research." In 2nd International Symposium on Computer, Communication, Control and Automation. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/isccca.2013.79.

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Kalcheva, Neli, Maya Todorova, and Ginka Marinova. "NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES." In 6th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2020. http://dx.doi.org/10.31410/eraz.2020.153.

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The purpose of the publication is to analyse popular classification algorithms in machine learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and AdaBoost Ensemble Algorithm. Their advantages and disadvantages are discussed. Research shows that there is no comprehensive universal method or algorithm for classification in machine learning. Each method or algorithm works well depending on the specifics of the task and the data used.
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Falaka, Bimo, Randy Erfa Saputra, Casi Setianingsih, and Muhammad Ary Murti. "Sea Wave Detection System Using Web-Based Naive Bayes Algorithm." In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2021. http://dx.doi.org/10.1109/icera53111.2021.9538697.

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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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Reports on the topic "Bayes Algorithm"

1

Lindell, Suzanne. Keyword Cluster Algorithm for Expert System Rule Bases. Fort Belvoir, VA: Defense Technical Information Center, June 1987. http://dx.doi.org/10.21236/ada183064.

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Dilworth, S. J., N. J. Kalton, D. Kutzarova, and V. N. Temlyakov. The Thresholding Greedy Algorithm, Greedy Bases and Duality. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada640677.

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Maschal, Jr, Young Robert A., Reynolds S. S., Krapels Joe, Fanning Keith, Corbin Jonathan, and Ted. Review of Bayer Pattern Color Filter Array (CFA) Demosaicing with New Quality Assessment Algorithms. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada513752.

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