scholarly journals bAIcis: A Novel Bayesian Network Structural Learning Algorithm and Its Comprehensive Performance Evaluation Against Open-Source Software

2020 ◽  
Vol 27 (5) ◽  
pp. 698-708
Author(s):  
Lixia Zhang ◽  
Leonardo O. Rodrigues ◽  
Niven R. Narain ◽  
Viatcheslav R. Akmaev
Keyword(s):  
Performance Evaluation ◽  
Bayesian Network ◽  
Open Source ◽  
Open Source Software ◽  
Learning Algorithm ◽  
Structural Learning ◽  
Comprehensive Performance

scholarly journals TagNN: A Code Tag Generation Technology for Resource Retrieval from Open-Source Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lingbin Zeng ◽  
Xin Guo ◽  
Cheng Yang ◽  
Yao Lu ◽  
Xiao Li
Keyword(s):  
Big Data ◽  
Open Source ◽  
Open Source Software ◽  
Learning Algorithm ◽  
Difficult Problem ◽  
Empirical Knowledge ◽  
Huge Number ◽  
Source Codes ◽  
Deep Learning Algorithm ◽  
Generation Technology

With the vigorous development of open-source software, a huge number of open-source projects and open-source codes have been accumulated in open-source big data, which contains a wealth of code resources. However, effectively and efficiently retrieving the relevant code snippets in such a large amount of open-source big data is an extremely difficult problem. There are usually large gaps between the user’s natural language description and the open-source code snippets. In this paper, we propose a novel code tag generation and code retrieval approach named TagNN, which combines software engineering empirical knowledge and a deep learning algorithm. The experimental results show that our method has good effects on code tag generation and code snippet retrieval.

A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data

2021 ◽  
pp. 1-14
Author(s):  
Yong Chen ◽  
Tianbao Zhang ◽  
Ruojun Wang ◽  
Lei Cai
Keyword(s):  
Bayesian Network ◽  
Learning Algorithm ◽  
Hill Climbing ◽  
Structural Learning ◽  
Engineering Systems ◽  
Limited Data ◽  
Model Complex ◽  
Complex Engineering ◽  
Complex Engineering Systems ◽  
Three Components

The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples.

scholarly journals A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2054
Author(s):  
Ming Li ◽  
Ren Zhang ◽  
Kefeng Liu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
Causal Reasoning ◽  
Learning Algorithm ◽  
Training Sample ◽  
Causal Analysis ◽  
Local Optimum ◽  
Structural Learning ◽  
Ensemble Learning Algorithm ◽  
Bagging Method

The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. Base learners of EL are trained by using various SS algorithms. Then, a new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. To verify the validity and feasibility of C-EL, we compare it with six different SS algorithms. The experiment results show that C-EL has high accuracy and a strong generalization ability. More importantly, it is capable of learning more accurate structures under the small training sample condition.

Network-Based Ranking for Open Source Software Developer Prediction

2018 ◽  
Vol 28 (06) ◽  
pp. 845-868
Author(s):  
Zhefu Wu ◽  
Jianan Li ◽  
Chenbo Fu ◽  
Qi Xuan ◽  
Yun Xiang
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Nearest Neighbor ◽  
Time Window ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Email Communication ◽  
Node Ranking ◽  
Project Data

Open source software (OSS) projects and communities are becoming increasingly popular and influential recently. Communications and collaborations are essential for the success of projects. Usually, the most active and productive programmers are awarded with promotion to developers. To more effectively manage and progress the projects, it is important and beneficial to rank the programmers and thus, predict the developer candidates. In this work, we propose to combine machine learning techniques with existing complex network node ranking algorithms to improve the prediction results. Specifically, we have made the following contributions: (1), we have designed a novel machine learning-based classifier with significantly improved prediction performance; (2), we have constructed and tested various networks built based on the programmer email communication information; and (3), we have used real-world project data to compare different techniques and validate our methods. Experimental results demonstrate that our technique reduces the error rate by 25% compared with the second best. Moreover, we discover that the [Formula: see text] nearest neighbor (KNN)-based machine learning algorithm and non-directional temporal network with a time window of 1–3 months give the best prediction results.

Open Source Software: A Survey from 10,000 Feet

2010 ◽  
Author(s):  
Stephanos Androutsellis-Theotokis
Keyword(s):  
Open Source ◽  
Open Source Software
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