Classifying univariate uncertain data
| Autor: | Ying-Ho Liu, Huei-Yu Fan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Association rule learning
Wilcoxon signed-rank test Uncertain data Computer science Univariate Probability density function 02 engineering and technology computer.software_genre Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pairwise comparison Data mining computer Statistical hypothesis testing |
| Zdroj: | Applied Intelligence. 51:2622-2650 |
| ISSN: | 1573-7497 0924-669X |
| Popis: | In the literature, univariate uncertain data has a quantitative interval for each attribute in each transaction, which is accompanied by a probability density function indicating the probability that each value in the interval exists and appears. To the best of our knowledge, classifying univariate uncertain data has thus far seldom been addressed in the literature. Here, we propose the AssoU2Classifier algorithm to address this research gap. The AssoU2Classifier algorithm retrieves association rules from the univariate uncertain data to serve as a classification model. In addition, the U2Pruning procedure is developed to prune the association rules. The U2Pruning procedure not only reduces the number of association rules, which considerably accelerates the classification process, but also achieves high classification accuracies. In the experiments, the AssoU2Classifier algorithm was compared with 14 existing algorithms on 12 modified UCI datasets. The AssoU2Classifier algorithm obtained better classification accuracy than the compared algorithms on most of the datasets. Statistical tests (Friedman test and pairwise Wilcoxon test) also justified the advantage of the AssoU2Classifier algorithm. In addition, the AssoU2Classifier algorithm also had average learning time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink