Genetic programming for high-dimensional imbalanced classification with a new fitness function and program reuse mechanism

Pei, Wenbin; Xue, Bing; Shang, Lin; Zhang, Mengjie

doi:10.1007/s00500-020-05056-7

Cited by 17 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genetic Algorithm (GA) is an algorithm that simulates the biological evolution process in nature and obtains the optimal value in the whole world. Firstly, a population was randomly initialized, and fitness function was used to evaluate the fitness of each individual [21][22][23]. Select the best with the selection function, and then cross generation, mutation of the children, repeat the cycle until the optimal solution is found.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

Yang¹

2022

Mobile Information Systems

View full text Add to dashboard Cite

With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people’s lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.

show abstract

Section: Mobile Information Systemsmentioning

confidence: 99%

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

Yang¹

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…It has been signi cantly expanded on the implication, the enlargement of the opinion form, and vital change in the review of the communication of online reviews and the rational interceptions [4]. At a particular moment, it poses various problems to certain communities of public reviews.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Public Opinion through Real-Time Monitoring using Multimodal Recurrent Neural Network

Gupta¹,

Limka²,

Hossain³

et al. 2023

Preprint

View full text Add to dashboard Cite

There is growing use of technology and presence of people online globally. Electronic platforms have also become the avenue for expression of opinions on emerging issues by people. However, some of the posts or comments posted online could sometimes be negative with far reaching effects on the community. The use of machine learning algorithms through sentiment analysis offers means that could be used to mine data and analyse opinions that emanate from online to reach decisions or monitor ethical compliance. This study presents an approach that uses a Multimodal fusion with Recurrent Neural Networks (M-RNN) to predict opinions through decision making and real time monitoring. The dataset was trained using standard methods like a decision tree classifier, and the M-RNN model achieved an accuracy of roughly 82.80%. The training model's relative average error was close to 0.503% when using the M-RNN methodology, and the training cycle only needs to be repeated 250 times to achieve satisfactory results, a figure that is low when compared to other conventional methods.

show abstract

“…Classification algorithms have attracted wide attention in the industry and academia, there are many researches on classification algorithms of imbalanced datasets in various fields. For example, [6,7,8,9] conducts in-depth research in medical diagnosis, financial risk prediction, personal credit analysis or financial analysis, and text classification. Learning based on imbalanced datasets tends to generate biased classifiers, which is not only the case with GP classifiers, but also with other classification algorithms such as K-NearestNeighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

An evolving ensembles method based on genetic programming and whale optimization algorithm for imbalanced datasets

Ji,

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

View full text Add to dashboard Cite

Genetic Programming (GP) algorithm has been successfully applied to classification tasks for many years, with a long history and remarkable effects. It is worth noting that GP tends to evolve biased classifiers when it is used for class imbalanced datasets. Biased classifiers are often unreliable and will seriously affect the classification performance. In addition, when GP is used to train the model to adapt to the high-dimensional imbalanced datasets, it is easy to be affected by the majority class and ignore the existence of the minority class. Therefore, classification tasks based on GP in high-dimensional and imbalanced datasets need to be further studied. In this paper, we propose a GP approach for imbalance classification tasks with the aim of improving the classification performance and minimizing the time consumption, named HGPWOA. A multi-objectivization fitness function is adopted to solve the problem, supplemented by Whale Optimization Algorithm (WOA) for feature selection, and then feature augmentation strategy is adopted to improve the classification efficiency. The method was tested on 5 imbalanced datasets. The experimental results show that this method is superior to the traditional classification algorithms, which provides a new way to study the class imbalance problems.

show abstract

Genetic programming for high-dimensional imbalanced classification with a new fitness function and program reuse mechanism

Cited by 17 publications

References 47 publications

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

Prediction of Public Opinion through Real-Time Monitoring using Multimodal Recurrent Neural Network

An evolving ensembles method based on genetic programming and whale optimization algorithm for imbalanced datasets

Contact Info

Product

Resources

About