COMPARISON OF ALGORITHMS BASED ON ROUGH SET THEORY FOR A 3-CLASS CLASSIFICATION
| Autor: | Yonca Yazirli, Betül Kan-Kilinç |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Class (set theory)
Computer science 020206 networking & telecommunications 02 engineering and technology Set (abstract data type) Standard error Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Attribute Reduction Rough Set Theory Classification Real Estate Rough set Algorithm Selection (genetic algorithm) Test data |
| DOI: | 10.5281/zenodo.3401362 |
| Popis: | There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others. |
| Databáze: | OpenAIRE |
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