Data Mining and Knowledge Discovery最新文献_第9页

Multiple-input neural networks for time series forecasting incorporating historical and prospective context 多输入神经网络时间序列预测结合历史和未来的背景

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-27 DOI: 10.1007/s10618-023-00984-y

João Palet, Vasco Manquinho, Rui Henriques

{"title":"Multiple-input neural networks for time series forecasting incorporating historical and prospective context","authors":"João Palet, Vasco Manquinho, Rui Henriques","doi":"10.1007/s10618-023-00984-y","DOIUrl":"https://doi.org/10.1007/s10618-023-00984-y","url":null,"abstract":"Abstract Individual and societal systems are open systems continuously affected by their situational context. In recent years, context sources have been increasingly considered in different domains to aid short and long-term forecasts of systems’ behavior. Nevertheless, available research generally disregards the role of prospective context, such as calendrical planning or weather forecasts. This work proposes a multiple-input neural architecture consisting of a sequential composition of long short-term memory units or temporal convolutional networks able to incorporate both historical and prospective sources of situational context to aid time series forecasting tasks. Considering urban case studies, we further assess the impact that different sources of external context have on medical emergency and mobility forecasts. Results show that the incorporation of external context variables, including calendrical and weather variables, can significantly reduce forecasting errors against state-of-the-art forecasters. In particular, the incorporation of prospective context, generally neglected in related work, mitigates error increases along the forecasting horizon.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"11 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136316825","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

Federated singular value decomposition for high-dimensional data 高维数据的联邦奇异值分解

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-26 DOI: 10.1007/s10618-023-00983-z

Anne Hartebrodt, Richard Röttger, David B. Blumenthal

引用次数: 0

Somtimes: self organizing maps for time series clustering and its application to serious illness conversations 有时:时间序列聚类的自组织地图及其在重症对话中的应用

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-20 DOI: 10.1007/s10618-023-00979-9

Ali Javed, Donna M. Rizzo, Byung Suk Lee, Robert Gramling

{"title":"Somtimes: self organizing maps for time series clustering and its application to serious illness conversations","authors":"Ali Javed, Donna M. Rizzo, Byung Suk Lee, Robert Gramling","doi":"10.1007/s10618-023-00979-9","DOIUrl":"https://doi.org/10.1007/s10618-023-00979-9","url":null,"abstract":"Abstract There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen self-organizing map (SOM) is an unsupervised artificial neural network for clustering, visualizing, and reducing the dimensionality of complex data. Like all clustering methods, it requires a measure of similarity between input data (in this work time series). Dynamic time warping (DTW) is one such measure, and a top performer that accommodates distortions when aligning time series. Despite its popularity in clustering, DTW is limited in practice because the runtime complexity is quadratic with the length of the time series. To address this, we present a new a self-organizing map for clustering TIME Series, called SOMTimeS, which uses DTW as the distance measure. The method has similar accuracy compared with other DTW-based clustering algorithms, yet scales better and runs faster. The computational performance stems from the pruning of unnecessary DTW computations during the SOM’s training phase. For comparison, we implement a similar pruning strategy for K-means, and call the latter K-TimeS. SOMTimeS and K-TimeS pruned 43% and 50% of the total DTW computations, respectively. Pruning effectiveness, accuracy, execution time and scalability are evaluated using 112 benchmark time series datasets from the UC Riverside classification archive, and show that for similar accuracy, a 1.8 $$times$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>×</mml:mo> </mml:math> speed-up on average for SOMTimeS and K-TimeS, respectively with that rates vary between 1 $$times$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>×</mml:mo> </mml:math> and 18 $$times$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>×</mml:mo> </mml:math> depending on the dataset. We also apply SOMTimeS to a healthcare study of patient-clinician serious illness conversations to demonstrate the algorithm’s utility with complex, temporally sequenced natural language.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513626","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

Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation 无偏机器学习算法评价基准数据集的最优选择

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-20 DOI: 10.1007/s10618-023-00957-1

João Luiz Junho Pereira, Kate Smith-Miles, Mario Andrés Muñoz, Ana Carolina Lorena

引用次数: 0

Mondrian forest for data stream classification under memory constraints 内存约束下数据流分类的蒙德里安森林

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-17 DOI: 10.1007/s10618-023-00970-4

Martin Khannouz, Tristan Glatard

引用次数: 0

Fast, accurate and explainable time series classification through randomization 通过随机化快速，准确和可解释的时间序列分类

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-16 DOI: 10.1007/s10618-023-00978-w

Nestor Cabello, Elham Naghizade, Jianzhong Qi, Lars Kulik

{"title":"Fast, accurate and explainable time series classification through randomization","authors":"Nestor Cabello, Elham Naghizade, Jianzhong Qi, Lars Kulik","doi":"10.1007/s10618-023-00978-w","DOIUrl":"https://doi.org/10.1007/s10618-023-00978-w","url":null,"abstract":"Abstract Time series classification (TSC) aims to predict the class label of a given time series, which is critical to a rich set of application areas such as economics and medicine. State-of-the-art TSC methods have mostly focused on classification accuracy, without considering classification speed. However, efficiency is important for big data analysis. Datasets with a large training size or long series challenge the use of the current highly accurate methods, because they are usually computationally expensive. Similarly, classification explainability, which is an important property required by modern big data applications such as appliance modeling and legislation such as the European General Data Protection Regulation , has received little attention. To address these gaps, we propose a novel TSC method – the Randomized-Supervised Time Series Forest (r-STSF). r-STSF is extremely fast and achieves state-of-the-art classification accuracy. It is an efficient interval-based approach that classifies time series according to aggregate values of the discriminatory sub-series (intervals). To achieve state-of-the-art accuracy, r-STSF builds an ensemble of randomized trees using the discriminatory sub-series. It uses four time series representations, nine aggregation functions and a supervised binary-inspired search combined with a feature ranking metric to identify highly discriminatory sub-series. The discriminatory sub-series enable explainable classifications. Experiments on extensive datasets show that r-STSF achieves state-of-the-art accuracy while being orders of magnitude faster than most existing TSC methods and enabling for explanations on the classifier decision.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113227","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: 1

Design and evaluation of highly accurate smart contract code vulnerability detection framework 高精度智能合约代码漏洞检测框架的设计与评估

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-13 DOI: 10.1007/s10618-023-00981-1

Sowon Jeon, Gilhee Lee, Hyoungshick Kim, Simon S. Woo

引用次数: 0

MODE-Bi-GRU: orthogonal independent Bi-GRU model with multiscale feature extraction MODE-Bi-GRU:具有多尺度特征提取的正交独立Bi-GRU模型

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-10-09 DOI: 10.1007/s10618-023-00964-2

Wei Wang, Wenhan Ruan, Xiangfu Meng

引用次数: 0

The art of centering without centering for robust principal component analysis 鲁棒主成分分析中不定心的定心方法