Enhancing interpretability of rule-based classifiers through feature graphs

• URL: http://arxiv.org/abs/2506.13903v1
• Date: Mon, 16 Jun 2025 18:29:37 GMT
• Title: Enhancing interpretability of rule-based classifiers through feature graphs
• Authors: Christel Sirocchi, Damiano Verda,
• Abstract summary: We propose a framework for estimating feature contributions in rule-based systems.<n>We also introduce a graph-based feature visualisation strategy.<n>We showcase our method's capability to uncover novel insights on the combined predictive value of clinical features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In domains where transparency and trustworthiness are crucial, such as healthcare, rule-based systems are widely used and often preferred over black-box models for decision support systems due to their inherent interpretability. However, as rule-based models grow complex, discerning crucial features, understanding their interactions, and comparing feature contributions across different rule sets becomes challenging. To address this, we propose a comprehensive framework for estimating feature contributions in rule-based systems, introducing a graph-based feature visualisation strategy, a novel feature importance metric agnostic to rule-based predictors, and a distance metric for comparing rule sets based on feature contributions. By experimenting on two clinical datasets and four rule-based methods (decision trees, logic learning machines, association rules, and neural networks with rule extraction), we showcase our method's capability to uncover novel insights on the combined predictive value of clinical features, both at the dataset and class-specific levels. These insights can aid in identifying new risk factors, signature genes, and potential biomarkers, and determining the subset of patient information that should be prioritised to enhance diagnostic accuracy. Comparative analysis of the proposed feature importance score with state-of-the-art methods on 15 public benchmarks demonstrates competitive performance and superior robustness. The method implementation is available on GitHub: https://github.com/ChristelSirocchi/rule-graph.

Related papers

• Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applications [5.692357910541593]
  This paper presents a comprehensive analysis of rule-based stuttering detection systems.<n>We propose an enhanced rule-based framework that incorporates speaking-rate normalization, multi-level acoustic feature analysis, and hierarchical decision structures.<n>We demonstrate that rule-based systems excel particularly in prolongation detection (97-99% accuracy) and provide stable performance across varying speaking rates.
  arXiv  Detail & Related papers  (2025-08-21T15:01:05Z)
• On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselines [48.688209040613216]
  We argue that a clinically normal but input-close counterfactual represents a more accurate representation of a meaningful absence of features in medical data.<n>We evaluate the approach on three distinct medical data sets and empirically demonstrate that counterfactual baselines yield more faithful and medically relevant attributions.
  arXiv  Detail & Related papers  (2025-08-20T07:13:41Z)
• Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI [1.1049608786515839]
  Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide.<n>We propose a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with Radial Basis Function (RBF) Networks to achieve high classification accuracy and enhanced interpretability.
  arXiv  Detail & Related papers  (2025-01-24T19:19:02Z)
• Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping [0.24578723416255746]
  Feature selection assumes a pivotal role in enhancing model interpretability. The accuracy gained from aggregating decision trees comes at the expense of interpretability. The study introduces novel methods to construct feature graphs from unsupervised random forests.
  arXiv  Detail & Related papers  (2024-04-27T12:47:37Z)
• Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities [4.351859373879489]
  Inherited biases in deep learning models can lead to disparities in prediction accuracy across protected groups. We propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups.
  arXiv  Detail & Related papers  (2024-03-27T02:13:20Z)
• Prospector Heads: Generalized Feature Attribution for Large Models & Data [82.02696069543454]
  We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods. We demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data.
  arXiv  Detail & Related papers  (2024-02-18T23:01:28Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics [80.07271410743806]
  We study the relationship between cross-domain learning (CD) and model fairness. We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks. Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
  arXiv  Detail & Related papers  (2023-03-25T09:34:05Z)
• Neural-based classification rule learning for sequential data [0.0]
  We propose a novel differentiable fully interpretable method to discover both local and global patterns for rule-based binary classification. It consists of a convolutional binary neural network with an interpretable neural filter and a training strategy based on dynamically-enforced sparsity. We demonstrate the validity and usefulness of the approach on synthetic datasets and on an open-source peptides dataset.
  arXiv  Detail & Related papers  (2023-02-22T11:05:05Z)
• Simple and Scalable Algorithms for Cluster-Aware Precision Medicine [0.0]
  We propose a simple and scalable approach to joint clustering and embedding. This novel, cluster-aware embedding approach overcomes the complexity and limitations of current joint embedding and clustering methods. Our approach does not require the user to choose the desired number of clusters, but instead yields interpretable dendrograms of hierarchically clustered embeddings.
  arXiv  Detail & Related papers  (2022-11-29T19:27:26Z)
• Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications [60.26261850082012]
  We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
  arXiv  Detail & Related papers  (2022-09-29T17:44:14Z)
• Discriminative Attribution from Counterfactuals [64.94009515033984]
  We present a method for neural network interpretability by combining feature attribution with counterfactual explanations. We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
  arXiv  Detail & Related papers  (2021-09-28T00:53:34Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.