Papers › Automatic support vector data description
Automatic support vector data description
Reza Sadeghi, Javad Hamidzadeh
Event handlers have wide range of applications such as medical assistant systems and fire suppression systems. These systems try to provide accurate responses based on the least information. Support vector data description (SVDD) is one of the appropriate tools for such detections, which should handle lack of information. Therefore, many efforts have been done to improve SVDD. Unfortunately, the existing descriptors suffer from weak data characteristic in sparse data sets and their tuning parameters are organized improperly. These issues cause reduction of accuracy in event handlers when they are faced with data shortage. Therefore, we propose automatic support vector data description (ASVDD) based on both validation degree, which is originated from fuzzy rough set to discover data characteristic, and assigning effective values for tuning parameters by chaotic bat algorithm. To evaluate the performance of ASVDD, several experiments have been conducted on various data sets of UCI repository. The experimental results demonstrate superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and AUC. In order to prove meaningful distinction between the accuracy results of the proposed method and the leading-edge ones, the Wilcoxon statistical test has been conducted.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Outlier Detection | Balance scale_class 1 | ASVDD | Average Accuracy | 99.03 | #1 of 1 | Archive leaderboard | report |
| Outlier Detection | Breast cancer Wisconsin_class 2 | ASVDD | Average Accuracy | 37.62 | #1 of 1 | Archive leaderboard | report |
| Outlier Detection | Breast cancer Wisconsin_class 4 | ASVDD | Average Accuracy | 65.60 | #1 of 1 | Archive leaderboard | report |
| Outlier Detection | Glass identification | ASVDD | Average Accuracy | 99.05 | #1 of 1 | Archive leaderboard | report |
| Outlier Detection | Ionosphere_class b | ASVDD | Average Accuracy | 86.33 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections