Explainable decision forest: Transforming a decision forest into an interpretable tree

Sagi, Omer; Rokach, Lior

doi:10.1016/j.inffus.2020.03.013

Cited by 130 publications

(48 citation statements)

References 29 publications

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“…Authors [8] developed reasoning through the use of visual indicators making the model interpretable. In [9] authors proposed an interpretable tree from a decision forest making understandable by humans. As proposed in [10,11] interpretable ML models helps develop a reasonable and data-driven decision support system that results in personalised decisions.…”

Section: Interpretable Machine Learningmentioning

confidence: 99%

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions

2021

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This study attempts to categorise research conducted in the area of: use of machine learning in healthcare, using a systematic mapping study methodology. In our attempt, we reviewed literature from top journals, articles, and conference papers by using the keywords use of machine learning in healthcare. We queried Google Scholar, resulted in 1400 papers, and then categorised the results on the basis of the objective of the study, the methodology adopted, type of problem attempted and disease studied. As a result we were able to categorize study in five different categories namely, interpretable ML, evaluation of medical images, processing of EHR, security/privacy framework, and transfer learning. In the study we also found that most of the authors have studied cancer, and one of the least studied disease was epilepsy, evaluation of medical images is the most researched and a new field of research, Interpretable ML/Explainable AI, is gaining momentum. Our basic intent is to provide a fair idea to future researchers about the field and future directions.

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Section: Interpretable Machine Learningmentioning

confidence: 99%

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions

2021

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“…The leaf nodes can also acquire an effective fault classification ability in the proposed tree-structured decision layer to deal with crossseverity fault diagnosis tasks, which lays the foundation for the better generalization of the model. However, weak knowledge learning ability has greatly limited its application for a long time [31][32][33]. Although decision trees are interpretable and simple to use, they are prone to overfitting, can be less robust to small changes in training data, and generally rely on heuristic algorithms.…”

Section: Proposed Deep Convolutional Tree-inspired Networkmentioning

confidence: 99%

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

Wang

et al. 2021

Front. Mech. Eng.

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The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

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“…To avoid the need for overfitting and parameter tuning, the algorithm optimizes the rule lists and reduces the hyperparameter that allows the model to consider the trade-off between complicacy and goodness of fit. Sagi, et al presented a method to transform decision forests into interpretable decision trees [29], which aims to maintain the predictive performance of decision forests and enable humans to understand the effective classification of decision forests.…”

Section: Interpretable Model Extractionmentioning

confidence: 99%

“…Many explanations for black-box models are achieved by feature visualization whether they are explainable modelbased or specific interpretable methods. We collect five methods including LIME [29], Anchors [30], CAM [71], Guided Grad-CAM [70], and meaningful perturbation [28] to show the interpretability.…”

Section: A Applications Of Feature Visualizationmentioning

confidence: 99%

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Review Study of Interpretation Methods for Future Interpretable Machine Learning

Zhou

2020

IEEE Access

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In recent years, black-box models have developed rapidly because of their high accuracy. Balancing the interpretability and accuracy is increasingly important. The lack of interpretability severely limits the application of the model in academia and industry. Despite the various interpretable machine learning methods, the perspective and meaning of the interpretation are also different. We provide a review of the current interpretable methods and divide them based on the model being applied. We divide them into two categories: interpretable methods with the self-explanatory model and interpretable methods with external co-explanation. And the interpretable methods with external co-explanation are further divided into subbranch methods based on instances, SHAP, knowledge graph, deep learning, and clustering model. The classification aims to help us understand the model characteristics applied in the interpretable method better. This survey makes the researcher find a suitable model to solve interpretability problems more easily. And the comparison experiments contribute to discovering complementary features from different methods. At the same time, we explore the future challenges and trends of interpretable machine learning to promote the development of interpretable machine learning. INDEX TERMS Deep learning, interpretable machine learning, interpretable methods with the selfexplanatory model, interpretable methods with external co-explanation, influential instance, knowledge graph.

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Explainable decision forest: Transforming a decision forest into an interpretable tree

Cited by 130 publications

References 29 publications

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

Review Study of Interpretation Methods for Future Interpretable Machine Learning

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