Ensemble of randomized soft decision trees for robust classification

Kumar, G. Kishor; Viswanath, P.; Rao, A. Ananda

doi:10.1007/s12046-016-0465-z

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Several algorithms can be applied within the decision tree technique to determine how to split a node into two sub-nodes, with the goal of enhancing the homogeneity of resulting sub-nodes [ 26 ]. The process of finding the most homogeneous sub-nodes entails dividing the nodes based on all variables and then selecting the optimal split [ 29 ]. For a visual representation of the training flow chart for the Decision Tree model utilized in this study, please refer to Fig.…”

Section: Methodsmentioning

confidence: 99%

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Teimourian,

Rohacs,

Dimililer

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Teimourian,

Rohacs,

Dimililer

et al. 2024

Heliyon

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“…Soft decision trees can also be ensembled via a similar scheme. Kumar et al [20] proposed an ensemble of soft decision trees for robust classification; Yıldız et al…”

Section: Ensemble Learning and Tree Ensemblementioning

confidence: 99%

SDTR: Soft Decision Tree Regressor for Tabular Data

Luo

Cheng

et al. 2021

IEEE Access

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Deep neural networks have been proved a success in multiple fields. However, researchers still favor traditional approaches to obtain more interpretable models, such as Bayesian methods and decision trees when processing heterogeneous tabular data. Such models are hard to differentiate, thus inconvenient to be integrated into end-to-end settings. On the other hand, traditional neural networks are differentiable but perform poorly on tabular data. We propose a hierarchical differentiable neural regression model, Soft Decision Tree Regressor (SDTR). SDTR imitates a binary decision tree by a differentiable neural network and is plausible for ensemble schemes like bagging and boosting. The SDTR method was evaluated on multiple tabular-based regression tasks (YearPredictionMSD, MSLR-Web10K, Yahoo LETOR, SARCOS and Wine quality). Its performance is comparable with non-differentiable models (gradient boosting decision trees) and better than uninterpretable models (regular FCNN). On top of that, it can produce fair results with a restricted number of parameters, only using a small forest or even a single tree. We also propose an "average entropy" metric to evaluate the level of interpretability of a trained, soft decision tree neural network. This metric also helps to select proper structure and hyperparameters for such networks.

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“…An ensemble of decision trees is a common technique used for increasing accuracy or robustness of prediction, which can be incorporated in SDTs (Rota Bulo and Kontschieder, 2014;Kontschieder et al, 2015;Kumar et al, 2016), giving rise to neural decision forests. Since more than one tree needs to be considered during the inference process, this might yield complications in the interpretability.…”

Section: Related Workmentioning

confidence: 99%

CDT: Cascading Decision Trees for Explainable Reinforcement Learning

Ding,

Hernandez-Leal,

Ding

et al. 2020

Preprint

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Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to several factors, one being the complexity of explaining neural networks decisions. Recently, a group of works have used decision-tree-based models to learn explainable policies. Soft decision trees (SDTs) and discretized differentiable decision trees (DDTs) have been demonstrated to achieve both good performance and share the benefit of having explainable policies. In this work, we further improve the results for tree-based explainable RL in both performance and explainability. Our proposal, Cascading Decision Trees (CDTs) apply representation learning on the decision path to allow richer expressivity. Empirical results show that in both situations, where CDTs are used as policy function approximators or as imitation learners to explain black-box policies, CDTs can achieve better performances with more succinct and explainable models than SDTs. As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.

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Ensemble of randomized soft decision trees for robust classification

Cited by 6 publications

References 30 publications

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

SDTR: Soft Decision Tree Regressor for Tabular Data

CDT: Cascading Decision Trees for Explainable Reinforcement Learning

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