Data Mining and Knowledge Discovery最新文献_第7页

Revealing the structural behaviour of Brunelleschi’s Dome with machine learning techniques 用机器学习技术揭示布鲁内莱斯基圆顶的结构行为

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-02-06 DOI: 10.1007/s10618-024-01004-3

Stefano Masini, Silvia Bacci, Fabrizio Cipollini, Bruno Bertaccini

{"title":"Revealing the structural behaviour of Brunelleschi’s Dome with machine learning techniques","authors":"Stefano Masini, Silvia Bacci, Fabrizio Cipollini, Bruno Bertaccini","doi":"10.1007/s10618-024-01004-3","DOIUrl":"https://doi.org/10.1007/s10618-024-01004-3","url":null,"abstract":"The Brunelleschi’s Dome is one of the most iconic symbols of the Renaissance and is among the largest masonry domes ever constructed. Since the late 17th century, first masonry cracks appeared on the Dome, giving the start to a monitoring activity. In modern times, since 1988 a monitoring system comprised of 166 electronic sensors, including deformometers and thermometers, has been in operation, providing a valuable source of real-time data on the monument’s health status. With the deformometers taking measurements at least four times per day, a vast amount of data is now available to explore the potential of the latest Artificial Intelligence and Machine Learning techniques in the field of historical-architectural heritage conservation. The objective of this contribution is twofold. Firstly, for the first time ever, we aim to unveil the overall structural behaviour of the Dome as a whole, as well as that of its specific sections (known as webs). We achieve this by evaluating the effectiveness of certain dimensionality reduction techniques on the extensive daily detections generated by the monitoring system, while also accounting for fluctuations in temperature over time. Secondly, we estimate a number of recurrent and convolutional neural network models to verify their capability for medium- and long-term prediction of the structural evolution of the Dome. We believe this contribution is an important step forward in the protection and preservation of historical buildings, showing the utility of machine learning in a context in which these are still little used.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"50 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139751786","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

VEM $$^2$$ L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion VEM $$^2$ L：在稀疏知识图谱完成上融合文本和结构知识的简单而有效的框架

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-02-06 DOI: 10.1007/s10618-023-01001-y

Tao He, Ming Liu, Yixin Cao, Meng Qu, Zihao Zheng, Bing Qin

{"title":"VEM $$^2$$ L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion","authors":"Tao He, Ming Liu, Yixin Cao, Meng Qu, Zihao Zheng, Bing Qin","doi":"10.1007/s10618-023-01001-y","DOIUrl":"https://doi.org/10.1007/s10618-023-01001-y","url":null,"abstract":"The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM(^2)L, a joint learning framework that incorporates structure and relevant text information to supplement insufficient features for sparse KGs. We begin by training two pre-existing KGC models: one based on structure and the other based on text. Our ultimate goal is to fuse knowledge acquired by these models. To achieve this, we divide knowledge within the models into two non-overlapping parts: expressive power and generalization ability. We then propose two different joint learning methods that co-distill these two kinds of knowledge respectively. For expressive power, we allow each model to learn from and exchange knowledge mutually on training examples. For the generalization ability, we propose a novel co-distillation strategy using the Variational EM algorithm on unobserved queries. Our proposed joint learning framework is supported by both detailed theoretical evidence and qualitative experiments, demonstrating its effectiveness.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"18 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139751835","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

MASS: distance profile of a query over a time series MASS：查询在时间序列中的距离分布图

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-02-05 DOI: 10.1007/s10618-024-01005-2

Sheng Zhong, Abdullah Mueen

引用次数: 0

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms 更好的树：关于分类决策树归纳算法超参数调整的实证研究

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-31 DOI: 10.1007/s10618-024-01002-5

Rafael Gomes Mantovani, Tomáš Horváth, André L. D. Rossi, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André C. P. L. F. de Carvalho

{"title":"Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms","authors":"Rafael Gomes Mantovani, Tomáš Horváth, André L. D. Rossi, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André C. P. L. F. de Carvalho","doi":"10.1007/s10618-024-01002-5","DOIUrl":"https://doi.org/10.1007/s10618-024-01002-5","url":null,"abstract":"Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default hyperparameters fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many hyperparameters need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate hyperparameters’ relevance using 94 classification datasets from OpenML. The experimental results point out that different hyperparameter profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the Irace. Finally, we found out that tuning a specific small subset of hyperparameters is a good alternative for achieving optimal predictive performance.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"13 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139658401","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

Central node identification via weighted kernel density estimation 通过加权核密度估计识别中心节点

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-31 DOI: 10.1007/s10618-024-01003-4

引用次数: 0

Fusing structural information with knowledge enhanced text representation for knowledge graph completion 融合结构信息与知识增强型文本表示法，促进知识图谱的完善

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-19 DOI: 10.1007/s10618-023-00998-6

Kang Tang, Shasha Li, Jintao Tang, Dong Li, Pancheng Wang, Ting Wang

{"title":"Fusing structural information with knowledge enhanced text representation for knowledge graph completion","authors":"Kang Tang, Shasha Li, Jintao Tang, Dong Li, Pancheng Wang, Ting Wang","doi":"10.1007/s10618-023-00998-6","DOIUrl":"https://doi.org/10.1007/s10618-023-00998-6","url":null,"abstract":"Although knowledge graphs store a large number of facts in the form of triplets, they are still limited by incompleteness. Hence, Knowledge Graph Completion (KGC), defined as inferring missing entities or relations based on observed facts, has long been a fundamental issue for various knowledge driven downstream applications. Prevailing KG embedding methods for KGC like TransE rely solely on mining structural information of existing facts, thus failing to handle generalization issue as they are inapplicable to unseen entities. Recently, a series of researches employ pre-trained encoders to learn textual representation for triples i.e., textual-encoding methods. While exhibiting great generalization for unseen entities, they are still inferior compared with above KG embedding based ones. In this paper, we devise a novel textual-encoding learning framework for KGC. To enrich textual prior knowledge for more informative prediction, it features three hierarchical maskings which can utilize far contexts of input text so that textual prior knowledge can be elicited. Besides, to solve predictive ambiguity caused by improper relational modeling, a relational-aware structure learning scheme is applied based on textual embeddings. Extensive experimental results on several popular datasets suggest the effectiveness of our approach even compared with recent state-of-the-arts in this task.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"31 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497515","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

Adaptive Bernstein change detector for high-dimensional data streams 用于高维数据流的自适应伯恩斯坦变化检测器

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-09 DOI: 10.1007/s10618-023-00999-5

Marco Heyden, Edouard Fouché, Vadim Arzamasov, Tanja Fenn, Florian Kalinke, Klemens Böhm

引用次数: 0

When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification 当图卷积遇到双重关注：利用多标签文本分类进行在线隐私披露检测

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-05 DOI: 10.1007/s10618-023-00992-y

Zhanbo Liang, Jie Guo, Weidong Qiu, Zheng Huang, Shujun Li

{"title":"When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification","authors":"Zhanbo Liang, Jie Guo, Weidong Qiu, Zheng Huang, Shujun Li","doi":"10.1007/s10618-023-00992-y","DOIUrl":"https://doi.org/10.1007/s10618-023-00992-y","url":null,"abstract":"With the rise of Web 2.0 platforms such as online social media, people’s private information, such as their location, occupation and even family information, is often inadvertently disclosed through online discussions. Therefore, it is important to detect such unwanted privacy disclosures to help alert people affected and the online platform. In this paper, privacy disclosure detection is modeled as a multi-label text classification (MLTC) problem, and a new privacy disclosure detection model is proposed to construct an MLTC classifier for detecting online privacy disclosures. This classifier takes an online post as the input and outputs multiple labels, each reflecting a possible privacy disclosure. The proposed presentation method combines three different sources of information, the input text itself, the label-to-text correlation and the label-to-label correlation. A double-attention mechanism is used to combine the first two sources of information, and a graph convolutional network is employed to extract the third source of information that is then used to help fuse features extracted from the first two sources of information. Our extensive experimental results, obtained on a public dataset of privacy-disclosing posts on Twitter, demonstrated that our proposed privacy disclosure detection method significantly and consistently outperformed other state-of-the-art methods in terms of all key performance indicators.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"80 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376729","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

CompTrails: comparing hypotheses across behavioral networks CompTrails：跨行为网络的假设比较

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-03 DOI: 10.1007/s10618-023-00996-8

Tobias Koopmann, Martin Becker, Florian Lemmerich, Andreas Hotho

{"title":"CompTrails: comparing hypotheses across behavioral networks","authors":"Tobias Koopmann, Martin Becker, Florian Lemmerich, Andreas Hotho","doi":"10.1007/s10618-023-00996-8","DOIUrl":"https://doi.org/10.1007/s10618-023-00996-8","url":null,"abstract":"The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"28 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139095516","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

Effective signal reconstruction from multiple ranked lists via convex optimization 通过凸优化从多个排序列表中有效重建信号

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-01-02 DOI: 10.1007/s10618-023-00991-z

{"title":"Effective signal reconstruction from multiple ranked lists via convex optimization","authors":"","doi":"10.1007/s10618-023-00991-z","DOIUrl":"https://doi.org/10.1007/s10618-023-00991-z","url":null,"abstract":"<h3>Abstract</h3> The ranking of objects is widely used to rate their relative quality or relevance across multiple assessments. Beyond classical rank aggregation, it is of interest to estimate the usually unobservable latent signals that inform a consensus ranking. Under the only assumption of independent assessments, which can be incomplete, we introduce indirect inference via convex optimization in combination with computationally efficient Poisson Bootstrap. Two different objective functions are suggested, one linear and the other quadratic. The mathematical formulation of the signal estimation problem is based on pairwise comparisons of all objects with respect to their rank positions. Sets of constraints represent the order relations. The transitivity property of rank scales allows us to reduce substantially the number of constraints associated with the full set of object comparisons. The key idea is to globally reduce the errors induced by the rankers until optimal latent signals can be obtained. Its main advantage is low computational costs, even when handling (n < < p) data problems. Exploratory tools can be developed based on the bootstrap signal estimates and standard errors. Simulation evidence, a comparison with the state-of-the-art rank centrality method, and two applications, one in higher education evaluation and the other in molecular cancer research, are presented.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"52 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083082","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