Int. J. Approx. Reason.最新文献

Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources 在融合观测、有偏和随机数据源的同时逼近反事实界限

Int. J. Approx. Reason. Pub Date : 2023-07-31 DOI: 10.48550/arXiv.2307.16577

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber

引用次数: 1

Incremental reduction methods based on granular ball neighborhood rough sets and attribute grouping 基于颗粒球邻域粗糙集和属性分组的增量约简方法

Int. J. Approx. Reason. Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4404785

Yan Li, Xiaoxue Wu, Xizhao Wang

引用次数: 1

Random sets, copulas and related sets of probability measures 随机集、关联集和相关的概率测度集

Int. J. Approx. Reason. Pub Date : 2023-06-01 DOI: 10.2139/ssrn.4364268

B. Schmelzer

引用次数: 2

Attribute reduction based on fusion information entropy 基于融合信息熵的属性约简

Int. J. Approx. Reason. Pub Date : 2023-05-01 DOI: 10.2139/ssrn.4334415

Xia Ji, Jie Li, Peng Zhao, Sheng Yao

引用次数: 1

Pseudo-Kleene algebras determined by rough sets 由粗糙集确定的伪kleene代数

Int. J. Approx. Reason. Pub Date : 2023-04-12 DOI: 10.48550/arXiv.2304.05641

J. Järvinen, S. Radeleczki

引用次数: 1

Robust optimization with belief functions 基于信念函数的鲁棒优化

Int. J. Approx. Reason. Pub Date : 2023-03-09 DOI: 10.48550/arXiv.2303.05067

M. Goerigk, R. Guillaume, A. Kasperski, Pawel Zieli'nski

引用次数: 0

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks YODO算法:贝叶斯网络灵敏度分析的有效计算框架

Int. J. Approx. Reason. Pub Date : 2023-02-01 DOI: 10.48550/arXiv.2302.00364

R. Ballester-Ripoll, Manuele Leonelli

{"title":"The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks","authors":"R. Ballester-Ripoll, Manuele Leonelli","doi":"10.48550/arXiv.2302.00364","DOIUrl":"https://doi.org/10.48550/arXiv.2302.00364","url":null,"abstract":"Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. Various sensitivity measures have been defined to quantify such influence, most commonly some function of the quantity of interest's partial derivative with respect to the network's conditional probabilities. However, computing these measures in large networks with thousands of parameters can become computationally very expensive. We propose an algorithm combining automatic differentiation and exact inference to efficiently calculate the sensitivity measures in a single pass. It first marginalizes the whole network once, using e.g. variable elimination, and then backpropagates this operation to obtain the gradient with respect to all input parameters. Our method can be used for one-way and multi-way sensitivity analysis and the derivation of admissible regions. Simulation studies highlight the efficiency of our algorithm by scaling it to massive networks with up to 100'000 parameters and investigate the feasibility of generic multi-way analyses. Our routines are also showcased over two medium-sized Bayesian networks: the first modeling the country-risks of a humanitarian crisis, the second studying the relationship between the use of technology and the psychological effects of forced social isolation during the COVID-19 pandemic. An implementation of the methods using the popular machine learning library PyTorch is freely available.","PeriodicalId":13685,"journal":{"name":"Int. J. Approx. Reason.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86717078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Alien Coding 外星人的编码

Int. J. Approx. Reason. Pub Date : 2023-01-27 DOI: 10.48550/arXiv.2301.11479

Thibault Gauthier, Miroslav Olsák, J. Urban

引用次数: 3

Factorizing Lattices by Interval Relations 用区间关系分解格

Int. J. Approx. Reason. Pub Date : 2022-12-20 DOI: 10.48550/arXiv.2212.10208

M. Koyda, Gerd Stumme

引用次数: 2

On Computing Probabilistic Abductive Explanations 论计算概率溯因解释

Int. J. Approx. Reason. Pub Date : 2022-12-12 DOI: 10.48550/arXiv.2212.05990

Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, Joao Marques-Silva

{"title":"On Computing Probabilistic Abductive Explanations","authors":"Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, Joao Marques-Silva","doi":"10.48550/arXiv.2212.05990","DOIUrl":"https://doi.org/10.48550/arXiv.2212.05990","url":null,"abstract":"The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {delta}, a set S of features is {delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.","PeriodicalId":13685,"journal":{"name":"Int. J. Approx. Reason.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74665823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7