scholarly journals Predicting Top-${L}$ Missing Links: An Improved Local Naïve Bayes Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 57868-57880 ◽  
Author(s):  
Longjie Li ◽  
Shijin Xu ◽  
Mingwei Leng ◽  
Shiyu Fang ◽  
Xiaoyun Chen
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Model ◽  
Naïve Bayes Model

scholarly journals Session Segmentation Method Based on Naïve Bayes Model

2012 ◽  
Vol 6-7 ◽  
pp. 576-582
Author(s):  
Ping Li ◽  
Ming Liang Cui ◽  
Zhen Shan Hou ◽  
Liu Liu Wei ◽  
Wen Hao Ying ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Time Interval ◽  
Segmentation Method ◽  
Retrieval Process ◽  
Search Activity ◽  
Query Suggestion ◽  
Bayes Model ◽  
Discrimination Model ◽  
Naïve Bayes Model

Session segmentation can not only contribute a lot to the further and deeper analysis of user’s search behavior but also act as the foundation of other retrieval process researches based on users’ complicated search behaviors. This paper proposes a session boundary discrimination model utilizing time interval and query likelihood on the basis of Naive Bayes Model. Compared with previous study, the model proposed in this paper shows a prominent improvement through experiment in three aspects, which is: recall ratio, precision ratio and value F. Owing to its advantage in session boundary discrimination, the application of the model can serve as a tool in fields like personalized information retrieval, query suggestion, search activity analysis and other fields which is related to search results improvement.

scholarly journals An Enhanced Naive Bayes Model for Dissolved Oxygen Forecasting in Shellfish Aquaculture

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 217917-217927
Author(s):  
Dashe Li ◽  
Jiajun Sun ◽  
Huanhai Yang ◽  
Xueying Wang
Keyword(s):  
Dissolved Oxygen ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Shellfish Aquaculture ◽  
Bayes Model ◽  
Naïve Bayes Model

Sign prediction by motif naive Bayes model in social networks

2020 ◽  
Vol 541 ◽  
pp. 316-331
Author(s):  
Si-Yuan Liu ◽  
Jing Xiao ◽  
Xiao-Ke Xu
Keyword(s):  
Social Networks ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Model ◽  
Naïve Bayes Model

scholarly journals A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mengmeng Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Information Diffusion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Structure Information ◽  
Bayes Model ◽  
Dynamic Social Network ◽  
Dynamic Social Networks ◽  
Text Information ◽  
Naïve Bayes Model ◽  
Tendency Analysis

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user’s retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user’s network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user’s retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

scholarly journals Morpheme Recovery Based on Naïve Bayes Model

2012 ◽  
Vol 19B (3) ◽  
pp. 195-200
Author(s):  
Jae-Hoon Kim ◽  
Kil-Ho Jeon
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Model ◽  
Naïve Bayes Model

Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic

2020 ◽  
Vol 2674 (9) ◽  
pp. 371-382
Author(s):  
Neeraj Saxena ◽  
Ruiyang Wang ◽  
Vinayak V. Dixit ◽  
S. Travis Waller
Keyword(s):  
Naive Bayes ◽  
Route Choice ◽  
Multinomial Logit ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Bayesian Approaches ◽  
Congested Traffic ◽  
Bayes Model ◽  
Stop And Go ◽  
Naïve Bayes Model

Driving in congested traffic is a nuisance that not only results in longer travel times, but also triggers frustration and impatience among drivers. A few studies have modeled the effects of congested traffic in the resulting route choice behavior of car drivers. The studies used frequentist models such as discrete choice models to analyze large samples. However, these studies did not compare the inferences obtained from the frequentist and Bayesian approaches, particularly for datasets which are not sufficiently large. It has been shown by researchers that Bayesian models perform well, especially when the sample size is small. Thus, this paper develops and compares a multinomial logit (frequentist) and a Naïve Bayes (Bayesian) model on a mid-sized dataset of size around 100 participants which was obtained from a driving simulator experiment to understand driver’s route choice under stop-and-go traffic. The results show that the prediction power of the Naïve Bayes model is much higher than the multinomial logit model (MNL). The Naïve Bayes model is also found to perform better than machine learning algorithms like the decision tree model. The findings from this study will be useful to researchers and practitioners as they should test both the approaches and select the appropriate model, particularly in the case of seemingly large datasets.

scholarly journals Out of Control: Growth and Indirect Discrimination in Ada and Canyon Counties' Rental Processes

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Berit Schaus ◽  
Sharon Hanson ◽  
Timothy Thomas
Keyword(s):  
Naive Bayes ◽  
Housing Prices ◽  
Naïve Bayes ◽  
Bayes Model ◽  
Control Growth ◽  
Indirect Discrimination ◽  
Geographical Regions ◽  
Naïve Bayes Model ◽  
The Impact ◽  
Minimal Research

Minimal research has been done on race in evictions and the rental process, and those studies that have been performed have been limited to narrow geographical regions (Thomas, 2017; Desmond, 2016). Additionally, research hasn’t been extended to Idaho, a state that is recognized for its rapid population growth. Research on evictions’ impact on New Americans is essentially nonexistent. Employing the Naïve Bayes Model, a modification of the Naïve Bayes Model, and content analysis of interviews, this study sought to uncover the impact of racial discrimination and growth in the eviction process. The results proved that black and Latinx minorities are evicted at higher rates than their white and Asian counterparts and that a lack of tenant protections and rapidly increasing housing prices, among other things, have played pivotal roles in Idaho’s eviction rates, specifically of racial minorities.

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