Pub Date : 2023-01-01DOI: 10.1007/978-3-031-30047-9_34
Loek Tonnaer, M. Holenderski, Vlado Menkovski
{"title":"Out-of-Distribution Generalisation with Symmetry-Based Disentangled Representations","authors":"Loek Tonnaer, M. Holenderski, Vlado Menkovski","doi":"10.1007/978-3-031-30047-9_34","DOIUrl":"https://doi.org/10.1007/978-3-031-30047-9_34","url":null,"abstract":"","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"30 1","pages":"433-445"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80547980","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}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-30047-9_28
Andraz Pelicon, Syrielle Montariol, Petra Kralj Novak
{"title":"Don't Start Your Data Labeling from Scratch: OpSaLa - Optimized Data Sampling Before Labeling","authors":"Andraz Pelicon, Syrielle Montariol, Petra Kralj Novak","doi":"10.1007/978-3-031-30047-9_28","DOIUrl":"https://doi.org/10.1007/978-3-031-30047-9_28","url":null,"abstract":"","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"159 1","pages":"353-365"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83324131","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}
Pub Date : 2022-12-08DOI: 10.48550/arXiv.2212.04183
Matthias Feurer, Katharina Eggensperger, Eddie Bergman, Florian Pfisterer, B. Bischl, F. Hutter
Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models, and the approximation of the Pareto front is used to assess their performance. In practice, we also want to measure generalization when moving from the validation to the test set. However, some of the models might no longer be Pareto-optimal which makes it unclear how to quantify the performance of the MHPO method when evaluated on the test set. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and studying its capabilities for comparing two optimization experiments.
{"title":"Mind the Gap: Measuring Generalization Performance Across Multiple Objectives","authors":"Matthias Feurer, Katharina Eggensperger, Eddie Bergman, Florian Pfisterer, B. Bischl, F. Hutter","doi":"10.48550/arXiv.2212.04183","DOIUrl":"https://doi.org/10.48550/arXiv.2212.04183","url":null,"abstract":"Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models, and the approximation of the Pareto front is used to assess their performance. In practice, we also want to measure generalization when moving from the validation to the test set. However, some of the models might no longer be Pareto-optimal which makes it unclear how to quantify the performance of the MHPO method when evaluated on the test set. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and studying its capabilities for comparing two optimization experiments.","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"412 1","pages":"130-142"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79931532","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}
Pub Date : 2022-11-24DOI: 10.48550/arXiv.2211.13681
David Schubert, Pritha Gupta, Marcel Wever
In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores based on metrics that can be computed with normal data only. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.
{"title":"Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets","authors":"David Schubert, Pritha Gupta, Marcel Wever","doi":"10.48550/arXiv.2211.13681","DOIUrl":"https://doi.org/10.48550/arXiv.2211.13681","url":null,"abstract":"In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores based on metrics that can be computed with normal data only. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"94 1","pages":"392-405"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88996623","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}
Pub Date : 2022-11-17DOI: 10.48550/arXiv.2211.09587
Inaam Ashraf, L. Hermes, André Artelt, Barbara Hammer
We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long-term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.
{"title":"Spatial Graph Convolution Neural Networks for Water Distribution Systems","authors":"Inaam Ashraf, L. Hermes, André Artelt, Barbara Hammer","doi":"10.48550/arXiv.2211.09587","DOIUrl":"https://doi.org/10.48550/arXiv.2211.09587","url":null,"abstract":"We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long-term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"12 1","pages":"29-41"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89495376","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}
Pub Date : 2022-08-02DOI: 10.1007/978-3-031-01333-1_9
R. Gaudel, Luis Galárraga, J. Delaunay, L. Rozé, Vaishnavi Bhargava
{"title":"s-LIME: Reconciling Locality and Fidelity in Linear Explanations","authors":"R. Gaudel, Luis Galárraga, J. Delaunay, L. Rozé, Vaishnavi Bhargava","doi":"10.1007/978-3-031-01333-1_9","DOIUrl":"https://doi.org/10.1007/978-3-031-01333-1_9","url":null,"abstract":"","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"8 1","pages":"102-114"},"PeriodicalIF":0.0,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81931415","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}
Pub Date : 2022-06-15DOI: 10.48550/arXiv.2206.07305
Andres F. Duque, Guy Wolf, Kevin R. Moon
The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domains of the entire dataset, which may be unrealistic. Furthermore, existing manifold alignment methods are not suited for cases where the data contains domain-specific regions, i.e., there is not a counterpart for a certain portion of the data in the other domain. We propose Diffusion Transport Alignment (DTA), a semi-supervised manifold alignment method that exploits prior correspondence knowledge between only a few points to align the domains. By building a diffusion process, DTA finds a transportation plan between data measured from two heterogeneous domains with different feature spaces, which by assumption, share a similar geometrical structure coming from the same underlying data generating process. DTA can also compute a partial alignment in a data-driven fashion, resulting in accurate alignments when some data are measured in only one domain. We empirically demonstrate that DTA outperforms other methods in aligning multimodal data in this semisupervised setting. We also empirically show that the alignment obtained by DTA can improve the performance of machine learning tasks, such as domain adaptation, inter-domain feature mapping, and exploratory data analysis, while outperforming competing methods.
当用不同的仪器或条件对某一现象进行研究，产生不同但相关的领域时，多模态数据的整合提出了挑战。许多现有的数据集成方法假设整个数据集的域之间存在已知的一对一对应关系，这可能是不现实的。此外，现有的流形对齐方法不适合数据包含特定于领域的区域的情况，即，在另一个领域中没有对应数据的特定部分。我们提出了扩散传输对齐(Diffusion Transport Alignment, DTA)，这是一种半监督流形对齐方法，它只利用几个点之间的先验对应知识来对齐域。通过建立一个扩散过程，DTA在两个具有不同特征空间的异构域之间找到一个传输计划，假设它们共享来自相同底层数据生成过程的相似几何结构。DTA还可以以数据驱动的方式计算部分对齐，从而在仅在一个域中测量某些数据时产生精确的对齐。我们的经验表明，在这种半监督设置中，DTA在对齐多模态数据方面优于其他方法。我们还通过经验证明，DTA获得的对齐可以提高机器学习任务的性能，如领域自适应、域间特征映射和探索性数据分析，同时优于竞争方法。
{"title":"Diffusion Transport Alignment","authors":"Andres F. Duque, Guy Wolf, Kevin R. Moon","doi":"10.48550/arXiv.2206.07305","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07305","url":null,"abstract":"The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domains of the entire dataset, which may be unrealistic. Furthermore, existing manifold alignment methods are not suited for cases where the data contains domain-specific regions, i.e., there is not a counterpart for a certain portion of the data in the other domain. We propose Diffusion Transport Alignment (DTA), a semi-supervised manifold alignment method that exploits prior correspondence knowledge between only a few points to align the domains. By building a diffusion process, DTA finds a transportation plan between data measured from two heterogeneous domains with different feature spaces, which by assumption, share a similar geometrical structure coming from the same underlying data generating process. DTA can also compute a partial alignment in a data-driven fashion, resulting in accurate alignments when some data are measured in only one domain. We empirically demonstrate that DTA outperforms other methods in aligning multimodal data in this semisupervised setting. We also empirically show that the alignment obtained by DTA can improve the performance of machine learning tasks, such as domain adaptation, inter-domain feature mapping, and exploratory data analysis, while outperforming competing methods.","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"26 1","pages":"116-129"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89533782","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}
Pub Date : 2022-02-19DOI: 10.1007/978-3-031-01333-1_13
Fabian Hinder, Valerie Vaquet, Barbara Hammer
{"title":"Suitability of Different Metric Choices for Concept Drift Detection","authors":"Fabian Hinder, Valerie Vaquet, Barbara Hammer","doi":"10.1007/978-3-031-01333-1_13","DOIUrl":"https://doi.org/10.1007/978-3-031-01333-1_13","url":null,"abstract":"","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"50 1","pages":"157-170"},"PeriodicalIF":0.0,"publicationDate":"2022-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81549171","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-01333-1_15
E. Lehembre, R. Bureau, B. Crémilleux, Bertrand Cuissart, J. Lamotte, Alban Lepailleur, Abdelkader Ouali, Albrecht Zimmermann
{"title":"Selecting Outstanding Patterns Based on Their Neighbourhood","authors":"E. Lehembre, R. Bureau, B. Crémilleux, Bertrand Cuissart, J. Lamotte, Alban Lepailleur, Abdelkader Ouali, Albrecht Zimmermann","doi":"10.1007/978-3-031-01333-1_15","DOIUrl":"https://doi.org/10.1007/978-3-031-01333-1_15","url":null,"abstract":"","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"77 1","pages":"185-198"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75520969","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-01333-1_4
Narjes Davari, Sepideh Pashami, Bruno Veloso, Sławomir Nowaczyk, Yuantao Fan, P. Pereira, Rita P. Ribeiro, J. Gama
{"title":"A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set","authors":"Narjes Davari, Sepideh Pashami, Bruno Veloso, Sławomir Nowaczyk, Yuantao Fan, P. Pereira, Rita P. Ribeiro, J. Gama","doi":"10.1007/978-3-031-01333-1_4","DOIUrl":"https://doi.org/10.1007/978-3-031-01333-1_4","url":null,"abstract":"","PeriodicalId":91439,"journal":{"name":"Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis","volume":"6 1","pages":"39-52"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78611358","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}
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