| Resumen |
The performance of machine learning algorithms can be optimized through the implementation of methodologies that facilitate the development of autonomous and adaptive behaviors. In this context, the incorporation of goal-oriented analysis is proposed as a means of effecting a transformation in the behavior of traditionally “passive" algorithms, such as Random Forest, through the endowment of proactivity. The aforementioned analysis, represented using the i* modeling language, identifies strategies that increase the diversity of generated trees and optimize their total number while preserving the original model’s effectiveness. In addition to the outcomes achieved, it is crucial to highlight that the goal-oriented methodology plays a pivotal role in the development and comprehension of novel algorithmic variants. Based on this analysis, two proactive variants were designed: the Proactive Forest and the Progressive Forest. These variants balance simplicity and effectiveness, maintaining the original algorithm’s performance while exploring more efficient configurations. This work introduces new variants of the Random Forest algorithm and demonstrates the potential of goal-oriented analysis as a methodology for guiding the design of more adaptive and effective algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. |
