Mixture of latent multinomial naive Bayes classifier

Harzevili, Nima Shiri; Alizadeh, Sasan H.

doi:10.1016/j.asoc.2018.04.020

Cited by 52 publications

(26 citation statements)

References 22 publications

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“…We adopt these two criteria so as to validate the effectiveness of the proposed method. (iv) Testing results of RBFN with different kernel‐clustering ways: From the results, we find that for most cases, results under the new RBF are better than those without RBF. Comparisons between our method and other methods, for example, Naïve Bayes [14, 15], AdaBoost [16] (the standard ensemble algorithm), C4.5 [17, 18] (the most popular decision tree method), K ‐means method [19] (for determining the hidden structure directly), the k ‐nearest neighbour algorithm (KNN) [19], MLP [20] (a method that uses back propagation to estimate RBFNN), and probabilistic NN (PNN) [21] are also given.…”

Section: Methodsmentioning

confidence: 99%

Influence of kernel clustering on an RBFN

Zhu

Miao

2019

CAAI Transactions on Intelligence Technology

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Section: Methodsmentioning

confidence: 99%

Influence of kernel clustering on an RBFN

Zhu

Miao

2019

CAAI Transactions on Intelligence Technology

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“…The basic assumption to use it is that each feature (Tweet) is independent and equal (no effect on each other and the same weight). This kind of algorithm has been used in many studies (Harzevili and Alizadeh 2018;Jiang et al 2016), and it has proven that accuracy, speed, and no excessive computational resources are not the only advantages. Experiments demonstrate that Bayes classifier is useful in many complex real-world situations and can outperform many similar purpose classifiers (Baesens et al 2003).…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

A comprehensive social media data processing and analytics architecture by using big data platforms: a case study of twitter flood-risk messages

Podhoranyi

2021

Earth Sci Inform

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The main objective of the article is to propose an advanced architecture and workflow based on Apache Hadoop and Apache Spark big data platforms. The primary purpose of the presented architecture is collecting, storing, processing, and analysing intensive data from social media streams. This paper presents how the proposed architecture and data workflow can be applied to analyse Tweets with a specific flood topic. The secondary objective, trying to describe the flood alert situation by using only Tweet messages and exploring the informative potential of such data is demonstrated as well. The predictive machine learning approach based on Bayes Theorem was utilized to classify flood and no flood messages. For this study, approximately 100,000 Twitter messages were processed and analysed. Messages were related to the flooding domain and collected over a period of 5 days (14 May-18 May 2018). Spark application was developed to run data processing commands automatically and to generate the appropriate output data. Results confirmed the advantages of many well-known features of Spark and Hadoop in social media data processing. It was noted that such technologies are prepared to deal with social media data streams, but there are still challenges that one has to take into account. Based on the flood tweet analysis, it was observed that Twitter messages with some considerations are informative enough to be used to estimate general flood alert situations in particular regions. Text analysis techniques proved that Twitter messages contain valuable flood-spatial information.

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“…Naive Bayes (NB) classifier is a simple but surprisingly powerful machine learning technique [ 11 , 12 ]. Despite its naive design and apparently oversimplified assumptions, NB has worked quite well in many complex real-world situations such as; real-time prediction, spam filtering, weather forecast, and medical diagnosis [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS 2 ) and Final Selection Stage (FS 2 ). IS 2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS 2 , which is the next stage of APSO. FS 2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution of this paper is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naïve Bayes Module (WNBM) and Distance Reinforcement Module (DRM). The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. DBNB has been compared with recent COVID-19 diagnose strategies. Experimental results have shown that DBNB outperforms recent COVID-19 diagnose strategies as it introduce the maximum accuracy with the minimum time penalty.

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Mixture of latent multinomial naive Bayes classifier

Cited by 52 publications

References 22 publications

Influence of kernel clustering on an RBFN

Influence of kernel clustering on an RBFN

A comprehensive social media data processing and analytics architecture by using big data platforms: a case study of twitter flood-risk messages

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

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