Document classification aims to assign a document to one or more categories based on its contents. This paper suggests the use of Field association (FA) words algorithm with Naïve Bayes Classifier to the problem of document categorization of Arabic language Our experimental study shows that using FA algorithm with Naïve Bayes (NB) Classifier gives the ~ 79% average accuracy and, using compound ...

The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage not properly managed it will become a to environment and humans. Managing issue city, municipalities aspects environment, logistics. An efficient supply chain with edge computing technology used managing operations include processing waste collection, transportation, Many research works ...

Designed for multi-relational explore and learn about important device data classification, and can be widely used in many fields. New classification algorithm Union, naive Bayes, which is the main function of what is known in the literature for the application of multiple classification Union relational environment. The results showed that naive Bayes achieves greater accuracy compared to exis...

Particle filter based on particle swarm optimization algorithm (PSO-PF) is not precise and trapping in local optimum easily, it is not able to satisfy the requirement of advanced integrated navigation system. In order to solve these problems, a novel particle filter algorithm based on dynamic neighborhood population adaptive particle swarm optimization (DPSO-PF) is presented in this paper. This...

This paper mainly deals with feature extraction algorithm used to improve the predicted accuracy of the classification. This paper applies with Principal Component analysis as a feature evaluator and ranker for searching method. Naive Bayes algorithm is used as a classification algorithm. It analyzes the hepatitis patients from the UC Irvine machine learning repository. The results of the class...

This paper introduces a new Bayesian network structure, named a Partial Bayesian Network (PBN), and describes an algorithm for constructing it. The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node. Initial experiments have compared the performance of the PBN algorithm with Naïve Bayes, Tree-A...

Data clustering, including problems such as finding network communities, can be put into a systematic framework by means of a Bayesian approach. Here we address the Bayesian formulation of the problem of finding hypergraph communities. We start by introducing a hypergraph generative model with a built-in group structure. Using a variational calculation we derive a variational Bayes algorithm, a...

In this paper, we introduce L1/Lp regularization of differences as a new regularization approach that can directly regularize models such as the naive Bayes classifier and (autoregressive) hidden Markov models. An algorithm is developed that selects values of the regularization parameter based on a derived stability condition. for the regularized naive Bayes classifier, we show that the method ...

In Chinese information processing, Naive Bayes is a simple text classification method that is easily implemented. Its core is the realization of the calculating posterior probability algorithm and the effectively reducing dimension for feature words. This paper improved Naive Bayes text classification from the calculating posterior probability and the reducing dimension of feature words of text...

Naive Bayes classifiers estimate posterior probabilities poorly (Zhang, 2004). In this paper, we propose a modification to the Naive Bayes classification algorithm which improves the classifier’s posterior probability estimates without affecting its performance. Since the modification involves the use of the reciprocal of the perplexity of the class-conditional feature probabilities, we call th...