Optimization of decision trees using modified African buffalo algorithm
| Autor: | Dharmpal D. Doye, Archana R. Panhalkar |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Optimization problem
General Computer Science Optimization algorithm Computer science Decision tree learning Decision tree Volume (computing) 020206 networking & telecommunications 02 engineering and technology Overfitting 0202 electrical engineering electronic engineering information engineering Extensive data 020201 artificial intelligence & image processing Algorithm Optimal decision |
| Zdroj: | Journal of King Saud University - Computer and Information Sciences. 34:4763-4772 |
| ISSN: | 1319-1578 |
| Popis: | Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. |
| Databáze: | OpenAIRE |
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