Computational Experimental Study on Social Organization Behavior Prediction Problems
Bibliographic Data
| ID | 22106643 |
|---|---|
| Authors | Weijin Jiang (0000-0002-6453-9158, Hunan University of Technology), Sijian Lv (0000-0002-7669-4809, Hunan University of Technology), Yan Wang (0009-0009-9633-0023, Hunan University of Technology), Yang Wang (0009-0007-0551-9820), Jiahui Chen (0000-0003-2516-0863, Hunan University of Technology), Xiaoliang Liu (0000-0001-5878-3990, Hunan University of Technology), Yongxia Sun (0000-0002-7359-3869, Hunan University of Technology) |
| Year | 2021 |
| Volume | 8 |
| Issue | 1 |
| Pages | 148-160 |
| Publication date | 2021-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2020.3017818 |
| OpenAlex | W3084459271 |
| Language | EN |
| Citations received | 1 |
| References cited | 17 |
With the development of mobile Internet, behavioral trajectories of human life are more and more recorded, which makes it possible to use computer technology to mine organizational behavior patterns. The mining of organizational behavior patterns based on social computing can not only prepare them in a targeted manner but also predict the consequences of possible measures. The organization behavior pattern mining has achieved a series of achievements in the fields of e-commerce and enterprise management. However, the problem of class imbalance and nonconsistent misclassification cost is common in the field of organizational behavior. For this problem, this article compares and analyzes the performance of the organizational behavior prediction model established by four typical cost-sensitive learning methods based on six classifiers, which provides a basis for the appropriate selection of cost-sensitive learning methods in different situations. Among them, the upsampling learning method is a better cost-sensitive learning method. However, there are some shortcomings in the upper sampling method. In order to avoid the possible overfitting problem of the social organization behavior prediction model established by the upper sampling method, this article proposes a new cost-sensitive learning method suitable for the mining of organizational behavior patterns. Based on the cost curve, this article proposes an effective personalized solution to the problem of class disequilibrium and nonconsistent misclassification cost in organizational behavior prediction modeling
Artificial neural network · Data mining · Machine learning · Overfitting · Computer Science · Cybercrime and Law Enforcement Studies · Data Mining Algorithms and Applications · Imbalanced Data Classification Techniques · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |