Improved version of explainable decision forest: Forest-Based Tree

Khalifa, Faten A.; Ali, Asmaa; Abdelkader, Hatem

doi:10.21608/ijci.2022.155977.1082

Cited by 1 publication

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“…Tose works that do not use rule extraction to explain ensembles for classifcation are [22,27]. Khalifa, Ali, and Abdel-Kader [23] propose a method to transform a learned ensemble of decision trees into a single decision tree; although they limit the prediction precision of their ensemble by ceiling the depth of their decision trees, their simplifed tree remains the same prediction precision as the ensemble is remarkable. To explain a stacked ensemble for classifcation, Silva, Fernandes, and Cardoso [27] present the results of several XAI methods-text-based rules extracted from a decision tree, feature importance from scorecards, and an example-based method-beside each other; the authors apply their explanation approach to several ensembles used in medicine and fnance.…”

Section: Explaining Ensembles As Shown In Tablementioning

confidence: 99%

An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival

Schleibaum,

Müller,

Sester

2024

Journal of Advanced Transportation

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Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction.

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