| Autor: |
Hichem, Haouassi, Rafik, MEHDAOUI, Ouahiba, Chouhal |
| Zdroj: |
International Journal of Swarm Intelligence Research (IJSIR); November 2021, Vol. 13 Issue: 1 p1-21, 21p |
| Abstrakt: |
Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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