Interpreting Deep Learning Model Using Rule-based Method

• URL: http://arxiv.org/abs/2010.07824v1
• Date: Thu, 15 Oct 2020 15:30:00 GMT
• Title: Interpreting Deep Learning Model Using Rule-based Method
• Authors: Xiaojian Wang, Jingyuan Wang, Ke Tang
• Abstract summary: We propose a multi-level decision framework to provide comprehensive interpretation for the deep neural network model. By fitting decision trees for each neuron and aggregate them together, a multi-level decision structure (MLD) is constructed at first. Experiments on the MNIST and National Free Pre-Pregnancy Check-up dataset are carried out to demonstrate the effectiveness and interpretability of MLD framework.
• Score: 36.01435823818395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models are favored in many research and industry areas and have reached the accuracy of approximating or even surpassing human level. However they've long been considered by researchers as black-box models for their complicated nonlinear property. In this paper, we propose a multi-level decision framework to provide comprehensive interpretation for the deep neural network model. In this multi-level decision framework, by fitting decision trees for each neuron and aggregate them together, a multi-level decision structure (MLD) is constructed at first, which can approximate the performance of the target neural network model with high efficiency and high fidelity. In terms of local explanation for sample, two algorithms are proposed based on MLD structure: forward decision generation algorithm for providing sample decisions, and backward rule induction algorithm for extracting sample rule-mapping recursively. For global explanation, frequency-based and out-of-bag based methods are proposed to extract important features in the neural network decision. Furthermore, experiments on the MNIST and National Free Pre-Pregnancy Check-up (NFPC) dataset are carried out to demonstrate the effectiveness and interpretability of MLD framework. In the evaluation process, both functionally-grounded and human-grounded methods are used to ensure credibility.

Related papers

• Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision~Boundary [3.66599628097164]
  We propose a novel approach to enhance the interpretability of deep binary classifiers. We select representative samples from the decision boundary and apply post-model explanation algorithms. Our work contributes to the responsible development and deployment of reliable machine learning systems.
  arXiv  Detail & Related papers  (2024-08-12T17:14:41Z)
• Learning by the F-adjoint [0.0]
  In this work, we develop and investigate this theoretical framework to improve some supervised learning algorithm for feed-forward neural network. Our main result is that by introducing some neural dynamical model combined by the gradient descent algorithm, we derived an equilibrium F-adjoint process. Experimental results on MNIST and Fashion-MNIST datasets, demonstrate that the proposed approach provide a significant improvements on the standard back-propagation training procedure.
  arXiv  Detail & Related papers  (2024-07-08T13:49:25Z)
• Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up [0.0]
  We present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Our proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the likelihood function dynamically during the Bayesian inference process. The implementation integrates with nested sampling algorithms and has been thoroughly evaluated using both simple cosmological dark energy models and diverse observational datasets.
  arXiv  Detail & Related papers  (2024-05-06T09:14:58Z)
• Manipulating Feature Visualizations with Gradient Slingshots [54.31109240020007]
  We introduce a novel method for manipulating Feature Visualization (FV) without significantly impacting the model's decision-making process. We evaluate the effectiveness of our method on several neural network models and demonstrate its capabilities to hide the functionality of arbitrarily chosen neurons.
  arXiv  Detail & Related papers  (2024-01-11T18:57:17Z)
• GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond [101.5329678997916]
  We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making. We propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR.
  arXiv  Detail & Related papers  (2022-11-03T16:42:40Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Acceleration techniques for optimization over trained neural network ensembles [1.0323063834827415]
  We study optimization problems where the objective function is modeled through feedforward neural networks with rectified linear unit activation. We present a mixed-integer linear program based on existing popular big-$M$ formulations for optimizing over a single neural network.
  arXiv  Detail & Related papers  (2021-12-13T20:50:54Z)
• Improving a neural network model by explanation-guided training for glioma classification based on MRI data [0.0]
  Interpretability methods have become a popular way to gain insight into the decision-making process of deep learning models. We propose a method for explanation-guided training that uses a Layer-wise relevance propagation (LRP) technique. We experimentally verified our method on a convolutional neural network (CNN) model for low-grade and high-grade glioma classification problems.
  arXiv  Detail & Related papers  (2021-07-05T13:27:28Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)
• Deep Unfolding Network for Image Super-Resolution [159.50726840791697]
  This paper proposes an end-to-end trainable unfolding network which leverages both learning-based methods and model-based methods. The proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model.
  arXiv  Detail & Related papers  (2020-03-23T17:55:42Z)

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.