Data Mining and Knowledge Discovery最新文献_第2页

Random walks with variable restarts for negative-example-informed label propagation 带可变重启的随机游走，用于负示例信息标签传播

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-13 DOI: 10.1007/s10618-024-01065-4

Sean Maxwell, Mehmet Koyutürk

{"title":"Random walks with variable restarts for negative-example-informed label propagation","authors":"Sean Maxwell, Mehmet Koyutürk","doi":"10.1007/s10618-024-01065-4","DOIUrl":"https://doi.org/10.1007/s10618-024-01065-4","url":null,"abstract":"Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network propagation), which provide limited ability to take into account negatively-labeled nodes (i.e., nodes that are known to be not associated with the label of interest). Specialized algorithms to incorporate negatively-labeled nodes generally focus on learning or readjusting the edge weights to drive walks away from negatively-labeled nodes and toward positively-labeled nodes. This approach has several disadvantages, as it increases the number of parameters to be learned, and does not necessarily drive the walk away from regions of the network that are rich in negatively-labeled nodes. We reformulate random walk with restarts and network propagation to enable “variable restarts\", that is the increased likelihood of restarting at a positively-labeled node when a negatively-labeled node is encountered. Based on this reformulation, we develop CusTaRd, an algorithm that effectively combines variable restart probabilities and edge re-weighting to avoid negatively-labeled nodes. To assess the performance of CusTaRd, we perform comprehensive experiments on network datasets commonly used in benchmarking label propagation and node classification algorithms. Our results show that CusTaRd consistently outperforms competing algorithms that learn edge weights or restart profiles, and that negatives close to positive examples are generally more informative than more distant negatives.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"41 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discord-based counterfactual explanations for time series classification 基于不和谐的时间序列分类反事实解释

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-07 DOI: 10.1007/s10618-024-01028-9

Omar Bahri, Peiyu Li, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

引用次数: 0

ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains ArcMatch：利用边域为带标记图提供高性能子图匹配

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-07 DOI: 10.1007/s10618-024-01061-8

Vincenzo Bonnici, Roberto Grasso, Giovanni Micale, Antonio di Maria, Dennis Shasha, Alfredo Pulvirenti, Rosalba Giugno

{"title":"ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains","authors":"Vincenzo Bonnici, Roberto Grasso, Giovanni Micale, Antonio di Maria, Dennis Shasha, Alfredo Pulvirenti, Rosalba Giugno","doi":"10.1007/s10618-024-01061-8","DOIUrl":"https://doi.org/10.1007/s10618-024-01061-8","url":null,"abstract":"Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to graphs with labels solely on vertices, our proposed approach, named can effectively handle graphs with labels on both vertices and edges. ntroduces an efficient new vertex/edge domain data structure filtering procedure to speed up subgraph queries. The procedure, called path-based reduction, filters initial domains by scanning them for paths up to a specified length that appear in the query graph. Additionally, ncorporates existing techniques like variable ordering and parent selection, as well as adapting the core search process, to take advantage of the information within edge domains. Experiments in real scenarios such as protein–protein interaction graphs, co-authorship networks, and email networks, show that s faster than state-of-the-art systems varying the number of distinct vertex labels over the whole target graph and query sizes.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"42 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On regime changes in text data using hidden Markov model of contaminated vMF distribution 利用受污染 vMF 分布的隐马尔可夫模型研究文本数据中的制度变化

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-03 DOI: 10.1007/s10618-024-01051-w

Yingying Zhang, Shuchismita Sarkar, Yuanyuan Chen, Xuwen Zhu

引用次数: 0

Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback 基于多臂匪帮的序列查询预测与变压器专家集合和即时反馈

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-02 DOI: 10.1007/s10618-024-01057-4

Shameem A. Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith

{"title":"Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback","authors":"Shameem A. Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith","doi":"10.1007/s10618-024-01057-4","DOIUrl":"https://doi.org/10.1007/s10618-024-01057-4","url":null,"abstract":"We study the problem of predicting the next query to be recommended in interactive data exploratory analysis to guide users to correct content. Current query prediction approaches are based on sequence-to-sequence learning, exploiting past interaction data. However, due to the resource-hungry training process, such approaches fail to adapt to immediate user feedback. Immediate feedback is essential and considered as a signal of the user’s intent. We contribute with a novel query prediction ensemble mechanism, which adapts to immediate feedback relying on multi-armed bandits framework. Our mechanism, an extension to the popular Exp3 algorithm, augments Transformer-based language models for query predictions by combining predictions from experts, thus dynamically building a candidate set during exploration. Immediate feedback is leveraged to choose the appropriate prediction in a probabilistic fashion. We provide comprehensive large-scale experimental and comparative assessment using a popular online literature discovery service, which showcases that our mechanism (i) improves the per-round regret substantially against state-of-the-art Transformer-based models and (ii) shows the superiority of causal language modelling over masked language modelling for query recommendations.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"44 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable and interpretable machine learning and data mining 可解释和可说明的机器学习和数据挖掘

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-30 DOI: 10.1007/s10618-024-01041-y

Martin Atzmueller, Johannes Fürnkranz, Tomáš Kliegr, Ute Schmid

引用次数: 0

Evaluating outlier probabilities: assessing sharpness, refinement, and calibration using stratified and weighted measures 评估离群值概率：使用分层和加权测量法评估清晰度、精细度和校准度

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-19 DOI: 10.1007/s10618-024-01056-5

Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek

{"title":"Evaluating outlier probabilities: assessing sharpness, refinement, and calibration using stratified and weighted measures","authors":"Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek","doi":"10.1007/s10618-024-01056-5","DOIUrl":"https://doi.org/10.1007/s10618-024-01056-5","url":null,"abstract":"An outlier probability is the probability that an observation is an outlier. Typically, outlier detection algorithms calculate real-valued outlier scores to identify outliers. Converting outlier scores into outlier probabilities increases the interpretability of outlier scores for domain experts and makes outlier scores from different outlier detection algorithms comparable. Although several transformations to convert outlier scores to outlier probabilities have been proposed in the literature, there is no common understanding of good outlier probabilities and no standard approach to evaluate outlier probabilities. We require that good outlier probabilities be sharp, refined, and calibrated. To evaluate these properties, we adapt and propose novel measures that use ground-truth labels indicating which observation is an outlier or an inlier. The refinement and calibration measures partition the outlier probabilities into bins or use kernel smoothing. Compared to the evaluation of probability in supervised learning, several aspects are relevant when evaluating outlier probabilities, mainly due to the imbalanced and often unsupervised nature of outlier detection. First, stratified and weighted measures are necessary to evaluate the probabilities of outliers well. Second, the joint use of the sharpness, refinement, and calibration errors makes it possible to independently measure the corresponding characteristics of outlier probabilities. Third, equiareal bins, where the product of observations per bin times bin length is constant, balance the number of observations per bin and bin length, allowing accurate evaluation of different outlier probability ranges. Finally, we show that good outlier probabilities, according to the proposed measures, improve the performance of the follow-up task of converting outlier probabilities into labels for outliers and inliers.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gradient-based explanation for non-linear non-parametric dimensionality reduction 基于梯度的非线性非参数降维解释

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-11 DOI: 10.1007/s10618-024-01055-6

Sacha Corbugy, Rebecca Marion, Benoît Frénay

{"title":"Gradient-based explanation for non-linear non-parametric dimensionality reduction","authors":"Sacha Corbugy, Rebecca Marion, Benoît Frénay","doi":"10.1007/s10618-024-01055-6","DOIUrl":"https://doi.org/10.1007/s10618-024-01055-6","url":null,"abstract":"Dimensionality reduction (DR) is a popular technique that shows great results to analyze high-dimensional data. Generally, DR is used to produce visualizations in 2 or 3 dimensions. While it can help understanding correlations between data, embeddings generated by DR are hard to grasp. The position of instances in low-dimension may be difficult to interpret, especially for non-linear, non-parametric DR techniques. Because most of the techniques are said to be neighborhood preserving (which means that explaining long distances is not relevant), some approaches try explaining them locally. These methods use simpler interpretable models to approximate the decision frontier locally. This can lead to misleading explanations. In this paper a novel approach to locally explain non-linear, non-parametric DR embeddings like t-SNE is introduced. It is the first gradient-based method for explaining these DR algorithms. The technique presented in this paper is applied on t-SNE, but is theoretically suitable for any DR method that is a minimization or maximization problem. The approach uses the analytical derivative of a t-SNE embedding to explain the position of an instance in the visualization.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"112 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network 通过生成式对抗网络对连续治疗的反事实推断进行去混淆表征学习

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-11 DOI: 10.1007/s10618-024-01058-3

Yonghe Zhao, Qiang Huang, Haolong Zeng, Yun Peng, Huiyan Sun

{"title":"De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network","authors":"Yonghe Zhao, Qiang Huang, Haolong Zeng, Yun Peng, Huiyan Sun","doi":"10.1007/s10618-024-01058-3","DOIUrl":"https://doi.org/10.1007/s10618-024-01058-3","url":null,"abstract":"Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are already some sample reweighting methods based on Marginal Structural Model for eliminating the confounding bias, they generally focus on removing the treatment’s linear dependence on confounders and rely on the accuracy of the assumed parametric models, which are usually unverifiable. In this paper, we propose a de-confounding representation learning (DRL) framework for counterfactual outcome estimation of continuous treatment by generating the representations of covariates decorrelated with the treatment variables. The DRL is a non-parametric model that eliminates both linear and nonlinear dependence between treatment and covariates. Specifically, we train the correlations between the de-confounding representations and the treatment variables against the correlations between the covariate representations and the treatment variables to eliminate confounding bias. Further, a counterfactual inference network is embedded into the framework to make the learned representations serve both de-confounding and trusted inference. Extensive experiments on synthetic and semi-synthetic datasets show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables. In addition, we apply the DRL model to a real-world medical dataset MIMIC III and demonstrate a detailed causal relationship between red cell width distribution and mortality.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"35 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing racism classification: an automatic multilingual data annotation system using self-training and CNN 加强种族主义分类：使用自我训练和 CNN 的自动多语言数据注释系统

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-11 DOI: 10.1007/s10618-024-01059-2

Ikram El Miqdadi, Soufiane Hourri, Fatima Zahra El Idrysy, Assia Hayati, Yassine Namir, Nikola S. Nikolov, Jamal Kharroubi

{"title":"Enhancing racism classification: an automatic multilingual data annotation system using self-training and CNN","authors":"Ikram El Miqdadi, Soufiane Hourri, Fatima Zahra El Idrysy, Assia Hayati, Yassine Namir, Nikola S. Nikolov, Jamal Kharroubi","doi":"10.1007/s10618-024-01059-2","DOIUrl":"https://doi.org/10.1007/s10618-024-01059-2","url":null,"abstract":"Accurate racism classification is crucial on social media, where racist and discriminatory content can harm individuals and society. Automated racism detection requires gathering and annotating a wide range of diverse and representative data as an essential source of information for the system. However, this task proves to be highly demanding in both time and resources, resulting in a significantly costly process. Moreover, racism can appear differently across languages because of the distinct cultural subtleties and vocabularies linked to each language. This necessitates having information resources in native languages to effectively detect racism, which further complicates constructing a database explicitly designed for identifying racism on social media platforms. In this study, an automated data annotation system for racism classification is presented, utilizing self-training and a combination of the Sentence-BERT (SBERT) transformers-based model for data representation and a Convolutional Neural Network (CNN) model. The system aids in the creation of a multilingual racism dataset consisting of 26,866 instances gathered from Facebook and Twitter. This is achieved through a self-training process that utilizes a labeled subset of the dataset to annotate the remaining unlabeled data. The study examines the impact of self-training on the system’s performance, revealing significant enhancements in model effectiveness. Especially for the English dataset, the system achieves a noteworthy accuracy rate of 92.53% and an F-score of 88.26%. The French dataset reaches an accuracy of 93.64% and an F-score of 92.68%. Similarly, for the Arabic dataset, the accuracy reaches 91.03%, accompanied by an F-score value of 92.15%. The implementation of self-training results in a remarkable 8–12% improvement in accuracy and F-score, as demonstrated in this study.\u0000","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"31 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0