| Autor: |
Bamhdi, Alwi M., Barros, Ana Luiza, Makda, Tahira Jehan, Fernandez, Marcial, Patel, Ahmed, Golafshan, Laleh |
| Předmět: |
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| Zdroj: |
International Journal of Advances in Soft Computing & Its Applications; Jul2024, Vol. 16 Issue 2, p263-296, 34p |
| Abstrakt: |
Data classification is a crucial aspect of knowledge discovery using machinelearning algorithm for supervised learning approach where the goal is to predict the categorical labels of new instances based on past observations. This research presents an innovative classification technique that utilizes Rough Set Attribute Reduction. The proposed method introduces the Re-heat Simulated Annealing (Re-heat SA) algorithm as a meta-heuristic approach. Rough set theory, a mathematical tool dealing with uncertainty and fuzziness in data, is employed to uncover hidden patterns in big data through feature selection. This paper introduces a novel meta-heuristic classification approach that utilizes rough set attribute reduction to achieve optimal accuracy. Re-heat SA effectively optimizes the problem by controlling the dependency degree to identify the minimal reducts required for classification prediction using the Rosetta software. Experimental results demonstrate that Re-heat SA outperforms comparable classification algorithms in discovering classification rules. The results reveal that three datasets achieved 100% accuracy, four datasets achieved accuracy rates ranging from 60% to 99%, and six datasets achieved accuracy rates ranging from 30% to 59%. Additionally, this paper discusses the need for standardization concerning the machine learning pipeline processes as big data and its handling grows exponentially. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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