Data Mining and Knowledge Discovery最新文献_第5页

quant: a minimalist interval method for time series classification 量化：用于时间序列分类的最小区间法

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-22 DOI: 10.1007/s10618-024-01036-9

Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb

引用次数: 0

MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting MSGNN：用于流行病预测的多尺度时空图神经网络

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-21 DOI: 10.1007/s10618-024-01035-w

Mingjie Qiu, Zhiyi Tan, Bing-Kun Bao

{"title":"MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting","authors":"Mingjie Qiu, Zhiyi Tan, Bing-Kun Bao","doi":"10.1007/s10618-024-01035-w","DOIUrl":"https://doi.org/10.1007/s10618-024-01035-w","url":null,"abstract":"Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) current models broaden receptive fields by scaling the depth of GNNs, which is insufficient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic patterns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific patterns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result. Code is available at https://github.com/JashinKorone/MSGNN.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"22 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150030","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

Unsupervised feature based algorithms for time series extrinsic regression 基于无监督特征的时间序列外回归算法

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-19 DOI: 10.1007/s10618-024-01027-w

David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva, Anthony Bagnall

{"title":"Unsupervised feature based algorithms for time series extrinsic regression","authors":"David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva, Anthony Bagnall","doi":"10.1007/s10618-024-01027-w","DOIUrl":"https://doi.org/10.1007/s10618-024-01027-w","url":null,"abstract":"Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, DrCIF is the only one that significantly outperforms a standard rotation forest regressor.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"32 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063378","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

Towards more sustainable and trustworthy reporting in machine learning 让机器学习报告更具可持续性和可信度

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-04-30 DOI: 10.1007/s10618-024-01020-3

Raphael Fischer, Thomas Liebig, Katharina Morik

{"title":"Towards more sustainable and trustworthy reporting in machine learning","authors":"Raphael Fischer, Thomas Liebig, Katharina Morik","doi":"10.1007/s10618-024-01020-3","DOIUrl":"https://doi.org/10.1007/s10618-024-01020-3","url":null,"abstract":"With machine learning (ML) becoming a popular tool across all domains, practitioners are in dire need of comprehensive reporting on the state-of-the-art. Benchmarks and open databases provide helpful insights for many tasks, however suffer from several phenomena: Firstly, they overly focus on prediction quality, which is problematic considering the demand for more sustainability in ML. Depending on the use case at hand, interested users might also face tight resource constraints and thus should be allowed to interact with reporting frameworks, in order to prioritize certain reported characteristics. Furthermore, as some practitioners might not yet be well-skilled in ML, it is important to convey information on a more abstract, comprehensible level. Usability and extendability are key for moving with the state-of-the-art and in order to be trustworthy, frameworks should explicitly address reproducibility. In this work, we analyze established reporting systems under consideration of the aforementioned issues. Afterwards, we propose STREP, our novel framework that aims at overcoming these shortcomings and paves the way towards more sustainable and trustworthy reporting. We use STREP’s (publicly available) implementation to investigate various existing report databases. Our experimental results unveil the need for making reporting more resource-aware and demonstrate our framework’s capabilities of overcoming current reporting limitations. With our work, we want to initiate a paradigm shift in reporting and help with making ML advances more considerate of sustainability and trustworthiness.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"8 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841715","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

Interpretable representations in explainable AI: from theory to practice 可解释人工智能中的可解释表征：从理论到实践

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-04-25 DOI: 10.1007/s10618-024-01010-5

Kacper Sokol, Peter Flach

{"title":"Interpretable representations in explainable AI: from theory to practice","authors":"Kacper Sokol, Peter Flach","doi":"10.1007/s10618-024-01010-5","DOIUrl":"https://doi.org/10.1007/s10618-024-01010-5","url":null,"abstract":"Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms. They translate the low-level data representation necessary for good predictive performance into high-level human-intelligible concepts used to convey the explanatory insights. Notably, the explanation type and its cognitive complexity are directly controlled by the interpretable representation, tweaking which allows to target a particular audience and use case. However, many explainers built upon interpretable representations overlook their merit and fall back on default solutions that often carry implicit assumptions, thereby degrading the explanatory power and reliability of such techniques. To address this problem, we study properties of interpretable representations that encode presence and absence of human-comprehensible concepts. We demonstrate how they are operationalised for tabular, image and text data; discuss their assumptions, strengths and weaknesses; identify their core building blocks; and scrutinise their configuration and parameterisation. In particular, this in-depth analysis allows us to pinpoint their explanatory properties, desiderata and scope for (malicious) manipulation in the context of tabular data where a linear model is used to quantify the influence of interpretable concepts on a black-box prediction. Our findings lead to a range of recommendations for designing trustworthy interpretable representations; specifically, the benefits of class-aware (supervised) discretisation of tabular data, e.g., with decision trees, and sensitivity of image interpretable representations to segmentation granularity and occlusion colour.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"50 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800893","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

Bake off redux: a review and experimental evaluation of recent time series classification algorithms 烘焙大赛再现：近期时间序列分类算法回顾与实验评估

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-04-19 DOI: 10.1007/s10618-024-01022-1

Matthew Middlehurst, Patrick Schäfer, Anthony Bagnall

{"title":"Bake off redux: a review and experimental evaluation of recent time series classification algorithms","authors":"Matthew Middlehurst, Patrick Schäfer, Anthony Bagnall","doi":"10.1007/s10618-024-01022-1","DOIUrl":"https://doi.org/10.1007/s10618-024-01022-1","url":null,"abstract":"In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a ‘bake off’, identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of feature they extract from time series data, forming a taxonomy of five main algorithm types. This categorisation of algorithms alongside the provision of code and accessible results for reproducibility has helped fuel an increase in popularity of the TSC field. Over six years have passed since this bake off, the UCR archive has expanded to 112 datasets and there have been a large number of new algorithms proposed. We revisit the bake off, seeing how each of the proposed categories have advanced since the original publication, and evaluate the performance of newer algorithms against the previous best-of-category using an expanded UCR archive. We extend the taxonomy to include three new categories to reflect recent developments. Alongside the originally proposed distance, interval, shapelet, dictionary and hybrid based algorithms, we compare newer convolution and feature based algorithms as well as deep learning approaches. We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category. Overall, we find that two recently proposed algorithms, MultiROCKET+Hydra (Dempster et al. 2022) and HIVE-COTEv2 (Middlehurst et al. Mach Learn 110:3211-3243. 2021), perform significantly better than other approaches on both the current and new TSC problems.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"54 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627694","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

Lost in the Forest: Encoding categorical variables and the absent levels problem 迷失在森林中分类变量编码和缺失水平问题

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-04-10 DOI: 10.1007/s10618-024-01019-w

Helen L. Smith, Patrick J. Biggs, Nigel P. French, Adam N. H. Smith, Jonathan C. Marshall

{"title":"Lost in the Forest: Encoding categorical variables and the absent levels problem","authors":"Helen L. Smith, Patrick J. Biggs, Nigel P. French, Adam N. H. Smith, Jonathan C. Marshall","doi":"10.1007/s10618-024-01019-w","DOIUrl":"https://doi.org/10.1007/s10618-024-01019-w","url":null,"abstract":"Levels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no satisfactory solution for absent levels in random forest models. Unlike missing data, absent levels are fully observed and known. Ordinal encoding of predictors allows absent levels to be integrated and used for prediction. Using a case study on source attribution of Campylobacter species using whole genome sequencing (WGS) data as predictors, we examine how target-agnostic versus target-based encoding of predictor variables with absent levels affects the accuracy of random forest models. We show that a target-based encoding approach using class probabilities, with absent levels designated the highest rank, is systematically biased, and that this bias is resolved by encoding absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordinal encoding predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are encoded according to their similarity to each of the other levels in the training data. We show that the PCO-encoding method performs at least as well as the target-based approach and is not biased.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"13 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562689","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

Time series clustering with random convolutional kernels 使用随机卷积核进行时间序列聚类

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-04-01 DOI: 10.1007/s10618-024-01018-x

引用次数: 0

Interpretable linear dimensionality reduction based on bias-variance analysis 基于偏差-方差分析的可解释线性降维

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-03-25 DOI: 10.1007/s10618-024-01015-0

{"title":"Interpretable linear dimensionality reduction based on bias-variance analysis","authors":"","doi":"10.1007/s10618-024-01015-0","DOIUrl":"https://doi.org/10.1007/s10618-024-01015-0","url":null,"abstract":"<h3>Abstract</h3> One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select a small number of the relevant, nonredundant features to preserve the complete information contained in the original dataset, with little collinearity among features. This procedure helps mitigate problems like overfitting and the curse of dimensionality, which arise when dealing with high-dimensional problems. On the other hand, it is not desirable to simply discard some features, since they may still contain information that can be exploited to improve results. Instead, dimensionality reduction techniques are designed to limit the number of features in a dataset by projecting them into a lower dimensional space, possibly considering all the original features. However, the projected features resulting from the application of dimensionality reduction techniques are usually difficult to interpret. In this paper, we seek to design a principled dimensionality reduction approach that maintains the interpretability of the resulting features. Specifically, we propose a bias-variance analysis for linear models and we leverage these theoretical results to design an algorithm, Linear Correlated Features Aggregation (LinCFA), which aggregates groups of continuous features with their average if their correlation is “sufficiently large”. In this way, all features are considered, the dimensionality is reduced and the interpretability is preserved. Finally, we provide numerical validations of the proposed algorithm both on synthetic datasets to confirm the theoretical results and on real datasets to show some promising applications.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"86 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300618","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

MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data MCCE：对表格数据的有效和现实的反事实解释进行蒙特卡洛采样

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-03-22 DOI: 10.1007/s10618-024-01017-y

Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland

{"title":"MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data","authors":"Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland","doi":"10.1007/s10618-024-01017-y","DOIUrl":"https://doi.org/10.1007/s10618-024-01017-y","url":null,"abstract":"We introduce MCCE: ({{{underline{varvec{M}}}}})onte ({{{underline{varvec{C}}}}})arlo sampling of valid and realistic ({{{underline{varvec{C}}}}})ounterfactual ({{{underline{varvec{E}}}}})xplanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by modeling the joint distribution of the mutable features given the immutable features and the decision. Unlike other on-manifold methods that tend to rely on variational autoencoders and have strict prediction model and data requirements, MCCE handles any type of prediction model and categorical features with more than two levels. MCCE first models the joint distribution of the features and the decision with an autoregressive generative model where the conditionals are estimated using decision trees. Then, it samples a large set of observations from this model, and finally, it removes the samples that do not obey certain criteria. We compare MCCE with a range of state-of-the-art on-manifold counterfactual methods using four well-known data sets and show that MCCE outperforms these methods on all common performance metrics and speed. In particular, including the decision in the modeling process improves the efficiency of the method substantially.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"365 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203508","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