scholarly journals Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Computation ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Korab Rrmoku ◽  
Besnik Selimi ◽  
Lule Ahmedi
Keyword(s):  
Recommender Systems ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Real Life ◽  
Naïve Bayes ◽  
Training Dataset ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Response Vector

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.

Implementation of Naive Bayes Classifier and Log Probabilistic for Book Classification Based on the Title

2018 ◽  
Vol 7 (4.44) ◽  
pp. 131
Author(s):  
Ridwan Rismanto ◽  
Dimas Wahyu Wibowo ◽  
Arie Rachmad Syulistyo
Keyword(s):  
Web Application ◽  
Teaching And Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Dataset ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Testing Dataset ◽  
Database Evaluation

Book is an important medium for teaching in higher education. It is facilitated by a library or a reading room which enabled student and teacher to fulfill their references for teaching and learning activities. For easy searching, each book classified by categories. In our institution, Information Technology Major of State Polytechnic of Malang, those categories are specifics to computer science topics. Every book entry need to be classified accordingly and to perform such task, one need to understand major keywords of the book title to correctly classify the books. The problem is, not all the librarian have such knowledge. Therefore manually classifying hundreds and even thousands of book is an exhausting work. This research is focused on automatic book classification based on its title using Naive Bayes Classifier and Log Probabilistic. The Log Probabilistic implementation is to solve the probability calculation result that is too small that cannot be represented in a computer programming floating points variable type. The algorithm then implemented in a web application using PHP and MySQL database. Evaluation has been done using Holdout method for 240 training dataset and 80 testing dataset resulting in 75% of accuration. We also tested the accuracy using K-fold Cross Validation resulting in 66.25% of accuration.  

scholarly journals Determination of near-fault impulsive signals with multivariate naïve Bayes method

2021 ◽  
Author(s):  
Deniz Ertuncay ◽  
Giovanni Costa
Keyword(s):  
Naive Bayes ◽  
Strong Motion ◽  
Naïve Bayes ◽  
Strike Slip ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Earthquake Physics ◽  
Near Fault ◽  
A Site

AbstractNear-fault ground motions may contain impulse behavior on velocity records. To calculate the probability of occurrence of the impulsive signals, a large dataset is collected from various national data providers and strong motion databases. The dataset has a large number of parameters which carry information on the earthquake physics, ruptured faults, ground motion parameters, distance between the station and several parts of the ruptured fault. Relation between the parameters and impulsive signals is calculated. It is found that fault type, moment magnitude, distance and azimuth between a site of interest and the surface projection of the ruptured fault are correlated with the impulsiveness of the signals. Separate models are created for strike-slip faults and non-strike-slip faults by using multivariate naïve Bayes classifier method. Naïve Bayes classifier allows us to have the probability of observing impulsive signals. The models have comparable accuracy rates, and they are more consistent on different fault types with respect to previous studies.

Sign in / Sign up

Export Citation Format

Share Document