Enhancing LIME using Neural Decision Trees

• URL: http://arxiv.org/abs/2603.20919v1
• Date: Sat, 21 Mar 2026 19:24:06 GMT
• Title: Enhancing LIME using Neural Decision Trees
• Abstract summary: Local Interpretable Model-Agnostic Explanations (LIME) has been a very popular framework for interpretable machine learning.<n>We propose the NDT-LIME variant that integrates Neural Decision Trees (NDTs) as surrogate models.<n>By leveraging the structured, hierarchical nature of NDTs, our approach aims at providing more accurate and meaningful local explanations.
• Score: 0.906681787649105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpreting complex machine learning models is a critical challenge, especially for tabular data where model transparency is paramount. Local Interpretable Model-Agnostic Explanations (LIME) has been a very popular framework for interpretable machine learning, also inspiring many extensions. While traditional surrogate models used in LIME variants (e.g. linear regression and decision trees) offer a degree of stability, they can struggle to faithfully capture the complex non-linear decision boundaries that are inherent in many sophisticated black-box models. This work contributes toward bridging the gap between high predictive performance and interpretable decision-making. Specifically, we propose the NDT-LIME variant that integrates Neural Decision Trees (NDTs) as surrogate models. By leveraging the structured, hierarchical nature of NDTs, our approach aims at providing more accurate and meaningful local explanations. We evaluate its effectiveness on several benchmark tabular datasets, showing consistent improvements in explanation fidelity over traditional LIME surrogates.

Related papers

• Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning [52.66717986145448]
  Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI)<n>This paper focuses on neural network (NN)-based generalized additive models (GAMs), a class of self-interpretable models.
  arXiv  Detail & Related papers  (2026-09-05T07:29:42Z)
• AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution [61.593935260052795]
  Post-training viaSupervised Fine-Tuning (SFT) and Reinforcement Learning (RL) is crucial for enhancing reasoning in Multimodal Large Language Models (MLLMs)<n>Existing paradigms often reach a performance bottleneck due to the limitations of static data.<n>We propose Anchor Evolution (AnE), a new paradigm that integrates truth-anchored data curation and model evolution.
  arXiv  Detail & Related papers  (2026-05-25T08:26:34Z)
• ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold [38.12595356036359]
  Tabular data is prevalent in high-stakes domains such as healthcare and finance.<n>While symbolic models offer verifiable logic, they lack semantic expressiveness.<n>We propose ReSS, a systematic framework that bridges symbolic and neural reasoning models.
  arXiv  Detail & Related papers  (2026-04-15T01:43:00Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• LLMs for Explainable AI: A Comprehensive Survey [0.7373617024876725]
  Large Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI)<n>LLMs transform complex machine learning outputs into easy-to-understand narratives.<n>LLMs can bridge the gap between sophisticated model behavior and human interpretability.
  arXiv  Detail & Related papers  (2025-03-31T18:19:41Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [86.76714527437383]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.<n>We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge.<n>Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• Latent Thought Models with Variational Bayes Inference-Time Computation [52.63299874322121]
  Latent Thought Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.<n>LTMs demonstrate superior sample and parameter efficiency compared to autoregressive models and discrete diffusion models.
  arXiv  Detail & Related papers  (2025-02-03T17:50:34Z)
• Subgroup Analysis via Model-based Rule Forest [0.0]
  Model-based Deep Rule Forests (mobDRF) is an interpretable representation learning algorithm designed to extract transparent models from data. We apply mobDRF to identify key risk factors for cognitive decline in an elderly population, demonstrating its effectiveness in subgroup analysis and local model optimization.
  arXiv  Detail & Related papers  (2024-08-27T13:40:15Z)
• SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction [15.832975722301011]
  We propose a novel method to enhance explainability with minimal accuracy loss. We have developed novel methods for estimating nodes by leveraging AI techniques. Our findings highlight the critical role that statistical methodologies can play in advancing explainable AI.
  arXiv  Detail & Related papers  (2024-06-16T14:43:01Z)
• Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets [46.19529338280716]
  Language models, characterized by their black-box nature, often hallucinate and display sensitivity to input perturbations.<n>We introduce a methodology designed to examine how input perturbations affect language models across various scales.<n>We present three distinct fine-tuning strategies to address robustness against multiple perturbations.
  arXiv  Detail & Related papers  (2023-11-15T02:59:10Z)
• Stable and Interpretable Deep Learning for Tabular Data: Introducing InterpreTabNet with the Novel InterpreStability Metric [4.362293468843233]
  We introduce InterpreTabNet, a model designed to enhance both classification accuracy and interpretability. We also present a novel evaluation metric, InterpreStability, which quantifies the stability of a model's interpretability.
  arXiv  Detail & Related papers  (2023-10-04T15:04:13Z)
• DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps [46.58231605323107]
  We propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models. DeforestVis helps users to explore the complexity versus fidelity trade-off by incrementally generating more stumps. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.
  arXiv  Detail & Related papers  (2023-03-31T21:17:15Z)
• Towards Interpretable Deep Learning Models for Knowledge Tracing [62.75876617721375]
  We propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model. Experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions.
  arXiv  Detail & Related papers  (2020-05-13T04:03:21Z)
• Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles [78.6363825307044]
  We propose Explainable Matrix (ExMatrix), a novel visualization method for Random Forest (RF) interpretability. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.
  arXiv  Detail & Related papers  (2020-05-08T21:03:48Z)
• Causality-aware counterfactual confounding adjustment for feature representations learned by deep models [14.554818659491644]
  Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML) We describe how a recently proposed counterfactual approach can still be used to deconfound the feature representations learned by deep neural network (DNN) models.
  arXiv  Detail & Related papers  (2020-04-20T17:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.