Related papers: Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs

Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs

URL: http://arxiv.org/abs/2407.07045v1
Date: Tue, 9 Jul 2024 17:05:52 GMT
Title: Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs
Authors: Christian Riefolo, Nicola Fanizzi, Claudia d'Amato,
Abstract summary: We describe an inductive approach based on learning simple belief networks. We show how such models can be converted into (probabilistic) axioms (or rules)
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Tackling the problem of learning probabilistic classifiers from incomplete data in the context of Knowledge Graphs expressed in Description Logics, we describe an inductive approach based on learning simple belief networks. Specifically, we consider a basic probabilistic model, a Naive Bayes classifier, based on multivariate Bernoullis and its extension to a two-tier network in which this classification model is connected to a lower layer consisting of a mixture of Bernoullis. We show how such models can be converted into (probabilistic) axioms (or rules) thus ensuring more interpretability. Moreover they may be also initialized exploiting expert knowledge. We present and discuss the outcomes of an empirical evaluation which aimed at testing the effectiveness of the models on a number of random classification problems with different ontologies.

Related papers

Estimating Causal Effects from Learned Causal Networks [56.14597641617531]
We propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We learn the causal Bayesian network and its confounding latent variables directly from the observational data. We show that this emphmodel completion learning approach can be more effective than estimand approaches.
arXiv Detail & Related papers (2024-08-26T08:39:09Z)
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals [4.384272169863716]
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. Attri-Net is an inherently interpretable model for multi-label classification that provides local and global explanations.
arXiv Detail & Related papers (2024-06-08T13:52:02Z)
CLIMAX: An exploration of Classifier-Based Contrastive Explanations [5.381004207943597]
We propose a novel post-hoc model XAI technique that provides contrastive explanations justifying the classification of a black box. Our method, which we refer to as CLIMAX, is based on local classifiers. We show that we achieve better consistency as compared to baselines such as LIME, BayLIME, and SLIME.
arXiv Detail & Related papers (2023-07-02T22:52:58Z)
Provable concept learning for interpretable predictions using variational inference [7.0349768355860895]
In safety critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. We propose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) We prove that our method is able to identify them while attaining optimal classification accuracy.
arXiv Detail & Related papers (2022-04-01T14:51:38Z)
Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
arXiv Detail & Related papers (2021-10-31T16:15:09Z)
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)
On the Transferability of Adversarial Attacksagainst Neural Text Classifier [121.6758865857686]
We investigate the transferability of adversarial examples for text classification models. We propose a genetic algorithm to find an ensemble of models that can induce adversarial examples to fool almost all existing models. We derive word replacement rules that can be used for model diagnostics from these adversarial examples.
arXiv Detail & Related papers (2020-11-17T10:45:05Z)
Open Set Recognition with Conditional Probabilistic Generative Models [51.40872765917125]
We propose Conditional Probabilistic Generative Models (CPGM) for open set recognition. CPGM can detect unknown samples but also classify known classes by forcing different latent features to approximate conditional Gaussian distributions. Experiment results on multiple benchmark datasets reveal that the proposed method significantly outperforms the baselines.
arXiv Detail & Related papers (2020-08-12T06:23:49Z)
Pairwise Supervision Can Provably Elicit a Decision Boundary [84.58020117487898]
Similarity learning is a problem to elicit useful representations by predicting the relationship between a pair of patterns. We show that similarity learning is capable of solving binary classification by directly eliciting a decision boundary.
arXiv Detail & Related papers (2020-06-11T05:35:16Z)
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach [9.978961706999833]
We consider the problem of exploring the prediction level sets of a classifier using probabilistic programming. We define a prediction level set to be the set of examples for which the predictor has the same specified prediction confidence. We demonstrate this technique with experiments on a synthetic dataset and MNIST.
arXiv Detail & Related papers (2020-01-09T15:57:51Z)

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