Machine Learning最新文献_第9页

Exposing and explaining fake news on-the-fly 即时揭露和解释假新闻

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-10 DOI: 10.1007/s10994-024-06527-w

Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo

{"title":"Exposing and explaining fake news on-the-fly","authors":"Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo","doi":"10.1007/s10994-024-06527-w","DOIUrl":"https://doi.org/10.1007/s10994-024-06527-w","url":null,"abstract":"Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80% accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":"36 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596312","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

Utilizing reinforcement learning for de novo drug design 利用强化学习进行新药设计

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-08 DOI: 10.1007/s10994-024-06519-w

Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani

{"title":"Utilizing reinforcement learning for de novo drug design","authors":"Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani","doi":"10.1007/s10994-024-06519-w","DOIUrl":"https://doi.org/10.1007/s10994-024-06519-w","url":null,"abstract":"Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules utilizing reinforcement learning. In this paper, we develop a unified framework for using reinforcement learning for de novo drug design, wherein we systematically study various on- and off-policy reinforcement learning algorithms and replay buffers to learn an RNN-based policy to generate novel molecules predicted to be active against the dopamine receptor DRD2. Our findings suggest that it is advantageous to use at least both top-scoring and low-scoring molecules for updating the policy when structural diversity is essential. Using all generated molecules at an iteration seems to enhance performance stability for on-policy algorithms. In addition, when replaying high, intermediate, and low-scoring molecules, off-policy algorithms display the potential of improving the structural diversity and number of active molecules generated, but possibly at the cost of a longer exploration phase. Our work provides an open-source framework enabling researchers to investigate various reinforcement learning methods for de novo drug design.","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":"43 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596434","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

Multi-consensus decentralized primal-dual fixed point algorithm for distributed learning 分布式学习的多共识分散原始二元定点算法

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-08 DOI: 10.1007/s10994-024-06537-8

Kejie Tang, Weidong Liu, Xiaojun Mao

引用次数: 0

Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach 学习非线性领域中的解释性逻辑规则：一种神经符号方法

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-08 DOI: 10.1007/s10994-024-06538-7

Andreas Bueff, Vaishak Belle

{"title":"Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach","authors":"Andreas Bueff, Vaishak Belle","doi":"10.1007/s10994-024-06538-7","DOIUrl":"https://doi.org/10.1007/s10994-024-06538-7","url":null,"abstract":"Deep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. Inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there’s a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision boundaries. In this research, we introduce a neuro-symbolic ILP framework, grounded on differentiable Neural Logic networks, tailored for non-linear rule extraction in mixed discrete-continuous spaces. Our methodology consists of a neuro-symbolic approach, emphasising the extraction of non-linear functions from mixed domain data. Our preliminary findings showcase our architecture’s capability to identify non-linear functions from continuous data, offering a new perspective in neural-symbolic research and underlining the adaptability of ILP-based frameworks for regression challenges in continuous scenarios.","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":"29 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596320","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

Generalization bounds for learning under graph-dependence: a survey 图依赖性下学习的泛化边界：一项调查

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-03 DOI: 10.1007/s10994-024-06536-9

Rui-Ray Zhang, Massih-Reza Amini

引用次数: 0

An effective keyword search co-occurrence multi-layer graph mining approach 一种有效的关键词搜索共现多层图挖掘方法

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-02 DOI: 10.1007/s10994-024-06528-9

Janet Oluwasola Bolorunduro, Zhaonian Zou, Mohamed Jaward Bah

{"title":"An effective keyword search co-occurrence multi-layer graph mining approach","authors":"Janet Oluwasola Bolorunduro, Zhaonian Zou, Mohamed Jaward Bah","doi":"10.1007/s10994-024-06528-9","DOIUrl":"https://doi.org/10.1007/s10994-024-06528-9","url":null,"abstract":"A combination of tools and methods known as \"graph mining\" is used to evaluate real-world graphs, forecast the potential effects of a given graph’s structure and properties for various applications, and build models that can yield actual graphs that closely resemble the structure seen in real-world graphs of interest. However, some graph mining approaches possess scalability and dynamic graph challenges, limiting practical applications. In machine learning and data mining, among the unique methods is graph embedding, known as network representation learning where representative methods suggest encoding the complicated graph structures into embedding by utilizing specific pre-defined metrics. Co-occurrence graphs and keyword searches are the foundation of search engine optimizations for diverse real-world applications. Current work on keyword searches on graphs is based on pre-established information retrieval search criteria and does not provide semantic linkages. Recent works on co-occurrence and keyword search methods function effectively on graphs with only one layer instead of many layers. However, the graph neural network has been utilized in recent years as a branch of graph model due to its excellent performance. This paper proposes an Effective Keyword Search Co-occurrence Multi-Layer Graph mining method by employing two core approaches: Multi-layer Graph Embedding and Graph Neural Networks. We conducted extensive tests using benchmarks on real-world data sets. Considering the experimental findings, the proposed method enhanced with the regularization approach is substantially excellent, with a 10% increment in precision, recall, and f1-score.","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":"32 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596326","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

Training data influence analysis and estimation: a survey 培训数据的影响分析和估计：一项调查

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-29 DOI: 10.1007/s10994-023-06495-7

Zayd Hammoudeh, Daniel Lowd

引用次数: 0

Machine learning with a reject option: a survey 带有拒绝选项的机器学习：一项调查

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-29 DOI: 10.1007/s10994-024-06534-x

Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis

引用次数: 0

Personalization for web-based services using offline reinforcement learning 利用离线强化学习实现网络服务个性化

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-28 DOI: 10.1007/s10994-024-06525-y

Pavlos Athanasios Apostolopoulos, Zehui Wang, Hanson Wang, Tenghyu Xu, Chad Zhou, Kittipate Virochsiri, Norm Zhou, Igor L. Markov

引用次数: 0

Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling? 跨域术语提取能否受益于跨语言转移和嵌套术语标注？

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-27 DOI: 10.1007/s10994-023-06506-7

Hanh Thi Hong Tran, Matej Martinc, Andraz Repar, Nikola Ljubešić, Antoine Doucet, Senja Pollak

{"title":"Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling?","authors":"Hanh Thi Hong Tran, Matej Martinc, Andraz Repar, Nikola Ljubešić, Antoine Doucet, Senja Pollak","doi":"10.1007/s10994-023-06506-7","DOIUrl":"https://doi.org/10.1007/s10994-023-06506-7","url":null,"abstract":"Automatic term extraction (ATE) is a natural language processing task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. In this paper, we treat ATE as a sequence-labeling task and explore the efficacy of XLMR in evaluating cross-lingual and multilingual learning against monolingual learning in the cross-domain ATE context. Additionally, we introduce NOBI, a novel annotation mechanism enabling the labeling of single-word nested terms. Our experiments are conducted on the ACTER corpus, encompassing four domains and three languages (English, French, and Dutch), as well as the RSDO5 Slovenian corpus, encompassing four additional domains. Results indicate that cross-lingual and multilingual models outperform monolingual settings, showcasing improved F1-scores for all languages within the ACTER dataset. When incorporating an additional Slovenian corpus into the training set, the multilingual model exhibits superior performance compared to state-of-the-art approaches in specific scenarios. Moreover, the newly introduced NOBI labeling mechanism enhances the classifier’s capacity to extract short nested terms significantly, leading to substantial improvements in Recall for the ACTER dataset and consequentially boosting the overall F1-score performance.","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":"32 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310898","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