Relevant bibliographies by topics / Naive bayes classifier

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

Academic literature on the topic 'Naive bayes classifier'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 May 2022

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Journal articles on the topic "Naive bayes classifier"

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Hairani, Hairani, Gibran Satya Nugraha, Mokhammad Nurkholis Abdillah, and Muhammad Innuddin. "Komparasi Akurasi Metode Correlated Naive Bayes Classifier dan Naive Bayes Classifier untuk Diagnosis Penyakit Diabetes." InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 3, no. 1 (September 13, 2018): 6–11. http://dx.doi.org/10.30743/infotekjar.v3i1.558.

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Penyakit diabetes merupakan salah satu penyakit paling banyak diderita oleh manusia seluruh dunia. Setiap tahun terjadi peningkatan kematian yang disebabkan oleh penyakit diabetes. Penyakit diabetes terjadi disebabkan oleh tubuh tidak menghasilkan insulin dalam jumlah yang cukup. Salah satu cara yang digunakan untuk mengurangi jumlah kematian yang disebabkan oleh penyakit diabetes adalah melakukan diagnosis secara dini. Salah satu teknik yang bisa digunakan adalah memanfaatkan teknik data mining. Untuk melakukan diagnosis penyakit diabetes dibutuhkan suatu metode yang memiliki akurasi terbaik. Pada penelitian ini melakukan komparasi metode Correlated-Naive Bayes Classifier dan Naive Bayes Classifier untuk mendapatkan akurasi terbaik sehingga dapat digunakan untuk diagnosis penyakit diabetes. Berdasarkan pengujian yang telah dilakukan menunjukkan bahwa metode Correlated Naive Bayes Classifier (CNBC) memperoleh akurasi terbaik dibandingkan dengan metode Naive Bayes Classifier (NBC) untuk Dataset Pima indian Diabetes. Tingkat akurasi metode Correlated Naive Bayes Classifier (CNBC) sebesar 67,15%, sedangkan metode Naive Bayes Classifier (NBC) sebesar 64,33%. Metode Correlated Naive Bayes Classifier (C-NBC) memiliki akurasi lebih tinggi dibandingkan metode Naïve Bayes Classifier (NBC) karena pada metode Correlated Naïve Bayes Classifier memperhitungkan nilai korelasi dari masing-masing atribut dataset terhadap Kelasnya. Dengan demikian penggunaan metode Correlated Naïve Bayes Classifier (C-NBC) dapat digunakan untuk melakukan diagnosis penyakit diabetes karena memiliki tingkat akurasi yang bagus dibandigkan metode Naive Bayes Classifier.
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Ahmad, Amir, Hamza Abujabal, and C. Aswani Kumar. "Random Subclasses Ensembles by Using 1-Nearest Neighbor Framework." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (February 24, 2017): 1750031. http://dx.doi.org/10.1142/s0218001417500318.

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A classifier ensemble is a combination of diverse and accurate classifiers. Generally, a classifier ensemble performs better than any single classifier in the ensemble. Naive Bayes classifiers are simple but popular classifiers for many applications. As it is difficult to create diverse naive Bayes classifiers, naive Bayes ensembles are not very successful. In this paper, we propose Random Subclasses (RS) ensembles for Naive Bayes classifiers. In the proposed method, new subclasses for each class are created by using 1-Nearest Neighbor (1-NN) framework that uses randomly selected points from the training data. A classifier considers each subclass as a class of its own. As the method to create subclasses is random, diverse datasets are generated. Each classifier in an ensemble learns on one dataset from the pool of diverse datasets. Diverse training datasets ensure diverse classifiers in the ensemble. New subclasses create easy to learn decision boundaries that in turn create accurate naive Bayes classifiers. We developed two variants of RS, in the first variant RS(2), two subclasses per class were created whereas in the second variant RS(4), four subclasses per class were created. We studied the performance of these methods against other popular ensemble methods by using naive Bayes as the base classifier. RS(4) outperformed other popular ensemble methods. A detailed study was carried out to understand the behavior of RS ensembles.
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Nakra, Abhilasha, and Manoj Duhan. "Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA." International Journal of Information Technology and Computer Science 11, no. 3 (March 8, 2019): 38–45. http://dx.doi.org/10.5815/ijitcs.2019.03.04.

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., Pooja, and Komal Kumar Bhatia. "Spam Detection using Naive Bayes Classifier." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 712–16. http://dx.doi.org/10.26438/ijcse/v6i7.712716.

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., Pooja, and Komal Kumar Bhatia. "Spam Detection using Naive Bayes Classifier." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 934–38. http://dx.doi.org/10.26438/ijcse/v6i7.934938.

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Setianingrum, Anif Hanifa, Dea Herwinda Kalokasari, and Imam Marzuki Shofi. "IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER." JURNAL TEKNIK INFORMATIKA 10, no. 2 (January 26, 2018): 109–18. http://dx.doi.org/10.15408/jti.v10i2.6822.

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ABSTRAK Informasi diperkirakan lebih dari 80% tersimpan dalam bentuk teks tidak terstruktur. Oleh karena itu, dibutuhkan sistem pengelolaan teks yaitu dengan metode text mining yang diyakini memiliki potensial nilai komersial tinggi. Salah satu implementasi dari text mining yaitu klasifikasi teks. Tidak hanya dokumen, pemanfaatan klasifikasi juga digunakan pada surat. Peneliti mengkaji Multinomial Naive Bayes Classifier untuk mengklasifikasi surat keluar sehingga dapat menentukan nomor surat secara otomatis. Sistem klasifikasi didukung dengan confix-stripping stemmer untuk menemukan kata dasar dan TF-IDF untuk pembobotan kata. Pengujian diukur dengan menggunakan confusion matrix. Dari hasil pengujian menunjukkan bahwa implementasi Multinomial Naive Bayes Classifier pada sistem klasifikasi surat memiliki tingkat accuracy, precision, recall, dan F-measure berturut-turut sebesar 89,58%, 79,17%, 78,72%, dan 77,05%. ABSTRACT The information estimated that more than 80% is stored in the form of unstructured text. Therefore, it takes a text management system, namely text mining method is believed to have high potential commercial. One of text mining implementation is text classification. Not only documents, the use of classification is also used in official letter. Researcher examined Multinomial Naive Bayes Classifier to classify the letter so it can determine the letters classification code automatically. The classification system is supported by confix-stripping stemmer to find root and TF-IDF for term weighting. The test used by confusion matrix of a classified as a measure of its quality. The test results showed that the implementation of Multinomial Naive Bayes Classifier on letter classification system has a level of accuracy, precision, recall, and F-measure respectively for 89.58%, 79.17%, 78.72% and 77.05%.How to Cite : Setianingrum, A. H. Kalokasari, D.H . Shofi. I. M. (2017). IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER. Jurnal Teknik Informatika, 10(2), 109-118. doi: 10.15408/jti.v10i2.6822Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i2.6822
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Utami, Putri Dinda, and Risna Sari. "Filtering Hoax Menggunakan Naive Bayes Classifier." MULTINETICS 4, no. 1 (May 30, 2018): 57. http://dx.doi.org/10.32722/multinetics.vol4.no.1.2018.pp.57-61.

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Utami, Putri Dinda, and Risna Sari. "Filtering Hoax Menggunakan Naive Bayes Classifier." MULTINETICS 4, no. 1 (May 30, 2018): 57. http://dx.doi.org/10.32722/vol4.no1.2018.pp57-61.

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Utami, Putri Dinda, and Risna Sari. "Filtering Hoax Menggunakan Naive Bayes Classifier." MULTINETICS 4, no. 1 (May 30, 2018): 57–61. http://dx.doi.org/10.32722/multinetics.v4i1.1179.

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Sugahara, Shouta, and Maomi Ueno. "Exact Learning Augmented Naive Bayes Classifier." Entropy 23, no. 12 (December 20, 2021): 1703. http://dx.doi.org/10.3390/e23121703.

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Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method.
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Dissertations / Theses on the topic "Naive bayes classifier"

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Wester, Philip. "Anomaly-based intrusion detection using Tree Augmented Naive Bayes Classifier." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295754.

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With the rise of information technology and the dependence on these systems, it becomes increasingly more important to keep the systems secure. The possibility to detect an intrusion with intrusion detection systems (IDS) is one of multiple fundamental technologies that may increase the security of a system. One of the bigger challenges of an IDS, is to detect types of intrusions that have previously not been encountered, so called unknown intrusions. These types of intrusions are generally detected by using methods collectively called anomaly detection methods. In this thesis I evaluate the performance of the algorithm Tree Augmented Naive Bayes Classifier (TAN) as an intrusion detection classifier. More specifically, I created a TAN program from scratch in Python and tested the program on two data sets containing data traffic. The thesis aims to create a better understanding of how TAN works and evaluate if it is a reasonable algorithm for intrusion detection. The results show that TAN is able to perform at an acceptable level with a reasonably high accuracy. The results also highlights the importance of using the smoothing operator included in the standard version of TAN.
Med informationsteknikens utveckling och det ökade beroendet av dessa system, blir det alltmer viktigt att hålla systemen säkra. Intrångsdetektionssystem (IDS) är en av många fundamentala teknologier som kan öka säkerheten i ett system. En av de större utmaningarna inom IDS, är att upptäcka typer av intrång som tidigare inte stötts på, så kallade okända intrång. Dessa intrång upptäcks oftast med hjälp av metoder som kollektivt kallas för avvikelsedetektionsmetoder. I denna uppsats utvärderar jag algoritmen Tree Augmented Naive Bayes Classifiers (TAN) prestation som en intrångsdetektionsklassificerare. Jag programmerade ett TAN-program, i Python, och testade detta program på två dataset som innehöll datatrafik. Denna uppsats ämnar att skapa en bättre förståelse för hur TAN fungerar, samt utvärdera om det är en lämplig algoritm för detektion av intrång. Resultaten visar att TAN kan prestera på en acceptabel nivå, med rimligt hög noggrannhet. Resultaten markerar även betydelsen av "smoothing operator", som inkluderas i standardversionen av TAN.
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Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.

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The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix against not helix (H/∼H), sheet against not sheet (S/∼S) and coil against not coil (C/∼C). The performance of these binary classifiers are compared using the overall accuracy in predicting the protein secondary structure for various window sizes. Our result indicate that hold-out cross validation achieved higher accuracy than 5-fold cross validation. The Naive Bayes classifier, using 5-fold cross validation achieved, the lowest accuracy for predicting helix against not helix. The classification tree classifiers, using 5-fold cross validation, achieved the lowest accuracies for both coil against not coil and sheet against not sheet classifications. The logistic regression classier accuracy is dependent on the window size; there is a positive relationship between the accuracy and window size. The logistic regression classier approach achieved the highest accuracy when compared to the classification tree and Naive Bayes classifiers for each classification task; predicting helix against not helix with accuracy 77.74 percent, for sheet against not sheet with accuracy 81.22 percent and for coil against not coil with accuracy 73.39 percent. It is noted that it is easier to compare classifiers if the classification process could be completely facilitated in R. Alternatively, it would be easier to assess these logistic regression classifiers if SPSS had a function to determine the accuracy of the logistic regression classifier.
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Koc, Levent. "Application of a Hidden Bayes Naive Multiclass Classifier in Network Intrusion Detection." The George Washington University, 2013.

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Vallin, Simon. "Likelihood-based classification of single trees in hemi-boreal forests." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-99691.

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Determining species of individual trees is important for forest management. In this thesis we investigate if it is possible to discriminate between Norway spruce, Scots pine and deciduous trees from airborne laser scanning data by using unique probability density functions estimated for each specie. We estimate the probability density functions in three different ways: by fitting a beta distribution, histogram density estimation and kernel density estimation. All these methods classifies single laser returns (and not segments of laser returns). The resulting classification is compared with a reference method based on features extracted from airborne laser scanning data.We measure how well a method performs by using the overall accuracy, that is the proportion of correctly predicted trees. The highest overall accuracy obtained by the methods we developed in this thesis is obtained by using histogram-density estimation where an overall accuracy of 83.4 percent is achieved. This result can be compared with the best result from the reference method that produced an overall accuracy of 84.1 percent. The fact that we achieve a high level of correctly classified trees indicates that it is possible to use these types of methods for identification of tree species.
Att kunna artbestämma enskilda träd är viktigt inom skogsbruket. I denna uppsats undersöker vi om det är möjligt att skilja mellan gran, tall och lövträd med data från en flygburen laserskanner genom att skatta en unik täthetsfunktion för varje trädslag. Täthetsfunktionerna skattas på tre olika sätt: genom att anpassa en beta-fördelning, skatta täthetsfunktionen med histogram samt skatta täthetsfunktionen med en kernel täthetsskattning. Alla dessa metoder klassificerar varje enskild laserretur (och inte segment av laserreturer). Resultaten från vår klassificering jämförs sedan med en referensmetod som bygger på särdrag från laserskanner data. Vi mäter hur väl metoderna presterar genom att jämföra den totala precisionen, vilket är andelen korrektklassificerade träd. Den högsta totala precisionen för de framtagna metoderna i denna uppsats erhölls med metoden som bygger på täthetsskattning med histogram. Precisionen för denna metod var 83,4 procent rättklassicerade träd. Detta kan jämföras med en rättklassificering på 84,1 procent vilket är det bästa resultatet för referensmetoderna. Att vi erhåller en så pass hög grad av rättklassificerade träd tyder på att de metoder som vi använder oss av är användbara för trädslagsklassificering.
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Warsitha, Tedy, and Robin Kammerlander. "Analyzing the ability of Naive-Bayes and Label Spreading to predict labels with varying quantities of training data : Classifier Evaluation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188132.

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A study was performed on Naive-Bayes and Label Spread- ing methods applied in a spam filter as classifiers. In the testing procedure their ability to predict was observed and the results were compared in a McNemar test; leading to the discovery of the strengths and weaknesses of the chosen methods in a environment of varying training data. Though the results were inconclusive due to resource restrictions, the theory is discussed from various angles in order to pro- vide a better understanding of the conditions that can lead to potentially different results between the chosen meth- ods; opening up for improvement and further studies. The conclusion made of this study is that a significant differ- ence exists in terms of ability to predict labels between the two classifiers. On a secondary note it is recommended to choose a classifier depending on available training data and computational power.
En studie utfördes på klassifieringsmetoderna Naive-Bayes och Label Spreading applicerade i ett spam filter. Meto- dernas förmåga att predicera observerades och resultaten jämfördes i ett McNemar test, vilket ledde till upptäckten av styrkorna och svagheterna av de valda metoderna i en miljö med varierande träningsdata. Fastän resultaten var ofullständiga på grund av bristfälliga resurser, så diskute- ras den bakomliggande teorin utifrån flera vinklar. Denna diskussion har målet att ge en bättre förståelse kring de bakomliggande förutsättningarna som kan leda till poten- tiellt annorlunda resultat för de valda metoderna. Vidare öppnar detta möjligheter för förbättringar och framtida stu- dier. Slutsatsen som dras av denna studie är att signifikanta skillnader existerar i förmågan att kunna predicera klasser mellan de två valda klassifierarna. Den slutgiltiga rekom- mendationen blir att välja en klassifierare utifrån utbudet av träningsdata och tillgängligheten av datorkraft.
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SILVA, Antonio Carlos de Castro da. "Reconhecimento automático de defeitos de fabricação em painéis TFT-LCD através de inspeção de imagem." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/17823.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-09-12T14:09:09Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MSc_Antonio Carlos de Castro da Silva_digital_12_04_16.pdf: 2938596 bytes, checksum: 9d5e96b489990fe36c4e1ad5a23148dd (MD5)
Made available in DSpace on 2016-09-12T14:09:09Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MSc_Antonio Carlos de Castro da Silva_digital_12_04_16.pdf: 2938596 bytes, checksum: 9d5e96b489990fe36c4e1ad5a23148dd (MD5) Previous issue date: 2016-01-15
A detecção prematura de defeitos nos componentes de linhas de montagem de fabricação é determinante para a obtenção de produtos finais de boa qualidade. Partindo desse pressuposto, o presente trabalho apresenta uma plataforma desenvolvida para detecção automática dos defeitos de fabricação em painéis TFT-LCD (Thin Film Transistor-Liquid Cristal Displays) através da realização de inspeção de imagem. A plataforma desenvolvida é baseada em câmeras, sendo o painel inspecionado posicionado em uma câmara fechada para não sofrer interferência da luminosidade do ambiente. As etapas da inspeção consistem em aquisição das imagens pelas câmeras, definição da região de interesse (detecção do quadro), extração das características, análise das imagens, classificação dos defeitos e tomada de decisão de aprovação ou rejeição do painel. A extração das características das imagens é realizada tomando tanto o padrão RGB como imagens em escala de cinza. Para cada componente RGB a intensidade de pixels é analisada e a variância é calculada, se um painel apresentar variação de 5% em relação aos valores de referência, o painel é rejeitado. A classificação é realizada por meio do algorítimo de Naive Bayes. Os resultados obtidos mostram um índice de 94,23% de acurácia na detecção dos defeitos. Está sendo estudada a incorporação da plataforma aqui descrita à linha de produção em massa da Samsung em Manaus.
The early detection of defects in the parts used in manufacturing assembly lines is crucial for assuring the good quality of the final product. Thus, this paper presents a platform developed for automatically detecting manufacturing defects in TFT-LCD (Thin Film Transistor-Liquid Cristal Displays) panels by image inspection. The developed platform is based on câmeras. The panel under inspection is positioned in a closed chamber to avoid interference from light sources from the environment. The inspection steps encompass image acquisition by the cameras, setting the region of interest (frame detection), feature extraction, image analysis, classification of defects, and decision making. The extraction of the features of the acquired images is performed using both the standard RGB and grayscale images. For each component the intensity of RGB pixels is analyzed and the variance is calculated. A panel is rejected if the value variation of the measure obtained is 5% of the reference values. The classification is performed using the Naive Bayes algorithm. The results obtained show an accuracy rate of 94.23% in defect detection. Samsung (Manaus) is considering the possibility of incorporating the platform described here to its mass production line.
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Pyon, Yoon Soo. "Variant Detection Using Next Generation Sequencing Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1347053645.

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Anderson, Michael P. "Bayesian classification of DNA barcodes." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2247.

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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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Giunchi, Massimiliano. "Tecnologie per la gestione di big data: analisi della piattaforma Hadoop." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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L’obiettivo del lavoro di tesi è approfondire i Big Data e le tecnologie idonee a trattarli con uno specifico focus su Hadoop, nonché eseguire una sperimentazione che concretizzi quanto esposto nei primi due punti. Per quel che concerne i Big Data è stata effettuata una panoramica delle principali caratteristiche, delle fonti che li generano e delle opportunità che offrono. Riguardo alle tecnologie che permettono di memorizzare ed elaborare Big Data, sono state analizzate alcune soluzioni offerte dal mercato: in questo ambito la più diffusa è rappresentata dalla piattaforma Hadoop implementata con varie modalità. Sono stati illustrati anche altri sistemi alternativi per la gestione dei Big Data quali i DBMS NoSQL. Il lavoro è proseguito con l’analisi dettagliata di Hadoop ossia il suo file system distribuito HDFS, il paradigma MapReduce e YARN che è il gestore delle risorse. La parte sperimentale è avvenuta in parallelo allo studio teorico: il primo passo è stato quello di installare Hadoop su un cluster. Poiché lo scopo consisteva nell’analizzare un set di dati proveniente da una tipica fonte di Big Data, la scelta in questo caso è ricaduta su Twitter e l’analisi che si è intrapresa è stata di sentiment analysis. Ciò ha comportato l’impiego di uno strumento per intercettare i dati, uno per elaborarli e successivamente uno per classificarli: Flume e Hive hanno reso possibile i primi due passi, mentre per compiere la classificazione si è ricorso ad un classificatore bayesiano-naif. Mahout è la libreria del framework che contiene alcuni algoritmi di machine learning tra cui anche quelli per la classificazione. Il lavoro è proseguito con la spiegazione del modello VSM per la rappresentazione dei documenti in formato vettoriale, dell’algortimo TF-IDF per la corretta attribuzione dei pesi al dizionario costruito e degli indici statistici necessari per valutare le prestazioni del classificatore. Infine sono stati mostrati i risultati ottenuti sui set di dati acquisiti.
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Book chapters on the topic "Naive bayes classifier"

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Mihaljevic, Bojan, Pedro Larrañaga, and Concha Bielza. "Augmented Semi-naive Bayes Classifier." In Advances in Artificial Intelligence, 159–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40643-0_17.

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Wajs, Wiesław, Marcin Ochab, Piotr Wais, Kamil Trojnar, and Hubert Wojtowicz. "Bronchopulmonary Dysplasia Prediction Using Naive Bayes Classifier." In Advances in Intelligent Systems and Computing, 281–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64474-5_23.

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Khan, Faheem Nawaz, Adnan Ali, Imtiaz Hussain, Nadeem Sarwar, and Hamaad Rafique. "Repairing Broken Links Using Naive Bayes Classifier." In Communications in Computer and Information Science, 461–72. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6052-7_40.

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Zhu, Wang-bin, Ya-ping Lin, Mu Lin, and Zhi-ping Chen. "Removing Smoothing from Naive Bayes Text Classifier." In Advances in Web-Age Information Management, 713–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11563952_69.

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Khanna, Dishant, and Arunima Sharma. "Kernel-Based Naive Bayes Classifier for Medical Predictions." In Advances in Intelligent Systems and Computing, 91–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7566-7_10.

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Talukdar, Gitimoni, Pranjal Protim Borah, and Arup Baruah. "Assamese Named Entity Recognition System Using Naive Bayes Classifier." In Communications in Computer and Information Science, 35–43. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1810-8_4.

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Li, Na, and Jin Han. "The Application of Naive Bayes Classifier in Name Disambiguation." In Cloud Computing and Security, 611–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68542-7_52.

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López-Cruz, Pedro L., Concha Bielza, and Pedro Larrañaga. "The von Mises Naive Bayes Classifier for Angular Data." In Advances in Artificial Intelligence, 145–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25274-7_15.

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Yin, Ling, and Richard Power. "Adapting the Naive Bayes Classifier to Rank Procedural Texts." In Lecture Notes in Computer Science, 179–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11735106_17.

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Muneeswari, G., D. Daniel, and K. Natarajan. "Activity Classifier: A Novel Approach Using Naive Bayes Classification." In Inventive Computation Technologies, 323–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33846-6_37.

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Conference papers on the topic "Naive bayes classifier"

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Azeraf, Elie, Emmanuel Monfrini, and Wojciech Pieczynski. "Improving Usual Naive Bayes Classifier Performances with Neural Naive Bayes based Models." In 11th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010890400003122.

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Surya, Prabha PM, and B. Subbulakshmi. "Sentimental Analysis using Naive Bayes Classifier." In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). IEEE, 2019. http://dx.doi.org/10.1109/vitecon.2019.8899618.

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Yang, Feng-Jen. "An Implementation of Naive Bayes Classifier." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00065.

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MORAES, R. M., and L. S. MACHADO. "A FUZZY EXPONENTIAL NAIVE BAYES CLASSIFIER." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0035.

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de Moraes, Ronei Marcos, Elaine Anita de Melo Gomes Soares, and Liliane dos Santos Machado. "A Fuzzy Gamma Naive Bayes classifier." In Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018). WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813273238_0088.

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Martinez-Arroyo, M., and L. E. Sucar. "Learning an Optimal Naive Bayes Classifier." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.748.

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Martinez-Arroyo, M., and L. E. Sucar. "Learning an Optimal Naive Bayes Classifier." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.749.

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Chen, Lei, Wei Lu, Liqiang Wang, Ergude Bao, Weiwei Xing, Yong Yang, and Victor Yuan. "Optimizing MapReduce Partitioner Using Naive Bayes Classifier." In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2017. http://dx.doi.org/10.1109/dasc-picom-datacom-cyberscitec.2017.138.

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Bina, Bahareh, Oliver Schulte, and Hassan Khosravi. "LNBC: A Link-Based Naive Bayes Classifier." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.116.

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Manap, Hany Hazfiza, Nooritawati Md Tahir, and R. Abdullah. "Anomalous gait detection using Naive Bayes classifier." In 2012 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2012). IEEE, 2012. http://dx.doi.org/10.1109/isiea.2012.6496664.

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