Data Mining and Knowledge Discovery最新文献_第2页

Detach-ROCKET: sequential feature selection for time series classification with random convolutional kernels Detach-ROCKET：利用随机卷积核进行时间序列分类的顺序特征选择

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-20 DOI: 10.1007/s10618-024-01062-7

Gonzalo Uribarri, Federico Barone, Alessio Ansuini, Erik Fransén

{"title":"Detach-ROCKET: sequential feature selection for time series classification with random convolutional kernels","authors":"Gonzalo Uribarri, Federico Barone, Alessio Ansuini, Erik Fransén","doi":"10.1007/s10618-024-01062-7","DOIUrl":"https://doi.org/10.1007/s10618-024-01062-7","url":null,"abstract":"Time Series Classification (TSC) is essential in fields like medicine, environmental science, and finance, enabling tasks such as disease diagnosis, anomaly detection, and stock price analysis. While machine learning models like Recurrent Neural Networks and InceptionTime are successful in numerous applications, they can face scalability issues due to computational requirements. Recently, ROCKET has emerged as an efficient alternative, achieving state-of-the-art performance and simplifying training by utilizing a large number of randomly generated features from the time series data. However, many of these features are redundant or non-informative, increasing computational load and compromising generalization. Here we introduce Sequential Feature Detachment (SFD) to identify and prune non-essential features in ROCKET-based models, such as ROCKET, MiniRocket, and MultiRocket. SFD estimates feature importance using model coefficients and can handle large feature sets without complex hyperparameter tuning. Testing on the UCR archive shows that SFD can produce models with better test accuracy using only 10% of the original features. We named these pruned models Detach-ROCKET. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy. On the largest binary UCR dataset, Detach-ROCKET improves test accuracy by 0.6% while reducing features by 98.9%. By enabling a significant reduction in model size without sacrificing accuracy, our methodology improves computational efficiency and contributes to model interpretability. We believe that Detach-ROCKET will be a valuable tool for researchers and practitioners working with time series data, who can find a user-friendly implementation of the model at https://github.com/gon-uri/detach_rocket.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"24 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192865","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

Bayesian network Motifs for reasoning over heterogeneous unlinked datasets 用于推理异构非链接数据集的贝叶斯网络动机

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-17 DOI: 10.1007/s10618-024-01054-7

Yi Sui, Alex Kwan, Alexander W. Olson, Scott Sanner, Daniel A. Silver

{"title":"Bayesian network Motifs for reasoning over heterogeneous unlinked datasets","authors":"Yi Sui, Alex Kwan, Alexander W. Olson, Scott Sanner, Daniel A. Silver","doi":"10.1007/s10618-024-01054-7","DOIUrl":"https://doi.org/10.1007/s10618-024-01054-7","url":null,"abstract":"Modern data-oriented applications often require integrating data from multiple heterogeneous sources. When these datasets share attributes, but are otherwise unlinked, there is no way to join them and reason at the individual level explicitly. However, as we show in this work, this does not prevent probabilistic reasoning over these heterogeneous datasets even when the data and shared attributes exhibit significant mismatches that are common in real-world data. Different datasets have different sample biases, disagree on category definitions and spatial representations, collect data at different temporal intervals, and mix aggregate-level with individual data. In this work, we demonstrate how a set of Bayesian network motifs allows all of these mismatches to be resolved in a composable framework that permits joint probabilistic reasoning over all datasets without manipulating, modifying, or imputing the original data, thus avoiding potentially harmful assumptions. We provide an open source Python tool that encapsulates our methodology and demonstrate this tool on a number of real-world use cases.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"125 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192866","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

Regularization-based methods for ordinal quantification 基于正则化的序量化方法

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-15 DOI: 10.1007/s10618-024-01067-2

Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani, Martin Senz

{"title":"Regularization-based methods for ordinal quantification","authors":"Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani, Martin Senz","doi":"10.1007/s10618-024-01067-2","DOIUrl":"https://doi.org/10.1007/s10618-024-01067-2","url":null,"abstract":"Quantification, i.e., the task of predicting the class prevalence values in bags of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for binary and multi-class problems in which the classes are not ordered. Here, we study the ordinal case, i.e., the case in which a total order is defined on the set of (n>2) classes. We give three main contributions to this field. First, we create and make available two datasets for ordinal quantification (OQ) research that overcome the inadequacies of the previously available ones. Second, we experimentally compare the most important OQ algorithms proposed in the literature so far. To this end, we bring together algorithms proposed by authors from very different research fields, such as data mining and astrophysics, who were unaware of each others’ developments. Third, we propose a novel class of regularized OQ algorithms, which outperforms existing algorithms in our experiments. The key to this gain in performance is that our regularization prevents ordinally implausible estimates, assuming that ordinal distributions tend to be smooth in practice. We informally verify this assumption for several real-world applications.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"75 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192892","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

Random walks with variable restarts for negative-example-informed label propagation 带可变重启的随机游走，用于负示例信息标签传播

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-13 DOI: 10.1007/s10618-024-01065-4

Sean Maxwell, Mehmet Koyutürk

{"title":"Random walks with variable restarts for negative-example-informed label propagation","authors":"Sean Maxwell, Mehmet Koyutürk","doi":"10.1007/s10618-024-01065-4","DOIUrl":"https://doi.org/10.1007/s10618-024-01065-4","url":null,"abstract":"Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network propagation), which provide limited ability to take into account negatively-labeled nodes (i.e., nodes that are known to be not associated with the label of interest). Specialized algorithms to incorporate negatively-labeled nodes generally focus on learning or readjusting the edge weights to drive walks away from negatively-labeled nodes and toward positively-labeled nodes. This approach has several disadvantages, as it increases the number of parameters to be learned, and does not necessarily drive the walk away from regions of the network that are rich in negatively-labeled nodes. We reformulate random walk with restarts and network propagation to enable “variable restarts\", that is the increased likelihood of restarting at a positively-labeled node when a negatively-labeled node is encountered. Based on this reformulation, we develop CusTaRd, an algorithm that effectively combines variable restart probabilities and edge re-weighting to avoid negatively-labeled nodes. To assess the performance of CusTaRd, we perform comprehensive experiments on network datasets commonly used in benchmarking label propagation and node classification algorithms. Our results show that CusTaRd consistently outperforms competing algorithms that learn edge weights or restart profiles, and that negatives close to positive examples are generally more informative than more distant negatives.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"41 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192895","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

Discord-based counterfactual explanations for time series classification 基于不和谐的时间序列分类反事实解释

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-07 DOI: 10.1007/s10618-024-01028-9

Omar Bahri, Peiyu Li, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

引用次数: 0

ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains ArcMatch：利用边域为带标记图提供高性能子图匹配

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-07 DOI: 10.1007/s10618-024-01061-8

Vincenzo Bonnici, Roberto Grasso, Giovanni Micale, Antonio di Maria, Dennis Shasha, Alfredo Pulvirenti, Rosalba Giugno

{"title":"ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains","authors":"Vincenzo Bonnici, Roberto Grasso, Giovanni Micale, Antonio di Maria, Dennis Shasha, Alfredo Pulvirenti, Rosalba Giugno","doi":"10.1007/s10618-024-01061-8","DOIUrl":"https://doi.org/10.1007/s10618-024-01061-8","url":null,"abstract":"Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to graphs with labels solely on vertices, our proposed approach, named can effectively handle graphs with labels on both vertices and edges. ntroduces an efficient new vertex/edge domain data structure filtering procedure to speed up subgraph queries. The procedure, called path-based reduction, filters initial domains by scanning them for paths up to a specified length that appear in the query graph. Additionally, ncorporates existing techniques like variable ordering and parent selection, as well as adapting the core search process, to take advantage of the information within edge domains. Experiments in real scenarios such as protein–protein interaction graphs, co-authorship networks, and email networks, show that s faster than state-of-the-art systems varying the number of distinct vertex labels over the whole target graph and query sizes.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"42 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935477","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

On regime changes in text data using hidden Markov model of contaminated vMF distribution 利用受污染 vMF 分布的隐马尔可夫模型研究文本数据中的制度变化

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-03 DOI: 10.1007/s10618-024-01051-w

Yingying Zhang, Shuchismita Sarkar, Yuanyuan Chen, Xuwen Zhu

引用次数: 0

Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback 基于多臂匪帮的序列查询预测与变压器专家集合和即时反馈

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-08-02 DOI: 10.1007/s10618-024-01057-4

Shameem A. Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith

{"title":"Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback","authors":"Shameem A. Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith","doi":"10.1007/s10618-024-01057-4","DOIUrl":"https://doi.org/10.1007/s10618-024-01057-4","url":null,"abstract":"We study the problem of predicting the next query to be recommended in interactive data exploratory analysis to guide users to correct content. Current query prediction approaches are based on sequence-to-sequence learning, exploiting past interaction data. However, due to the resource-hungry training process, such approaches fail to adapt to immediate user feedback. Immediate feedback is essential and considered as a signal of the user’s intent. We contribute with a novel query prediction ensemble mechanism, which adapts to immediate feedback relying on multi-armed bandits framework. Our mechanism, an extension to the popular Exp3 algorithm, augments Transformer-based language models for query predictions by combining predictions from experts, thus dynamically building a candidate set during exploration. Immediate feedback is leveraged to choose the appropriate prediction in a probabilistic fashion. We provide comprehensive large-scale experimental and comparative assessment using a popular online literature discovery service, which showcases that our mechanism (i) improves the per-round regret substantially against state-of-the-art Transformer-based models and (ii) shows the superiority of causal language modelling over masked language modelling for query recommendations.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"44 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885106","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

Explainable and interpretable machine learning and data mining 可解释和可说明的机器学习和数据挖掘

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-30 DOI: 10.1007/s10618-024-01041-y

Martin Atzmueller, Johannes Fürnkranz, Tomáš Kliegr, Ute Schmid

引用次数: 0

Evaluating outlier probabilities: assessing sharpness, refinement, and calibration using stratified and weighted measures 评估离群值概率：使用分层和加权测量法评估清晰度、精细度和校准度

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-07-19 DOI: 10.1007/s10618-024-01056-5

Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek

{"title":"Evaluating outlier probabilities: assessing sharpness, refinement, and calibration using stratified and weighted measures","authors":"Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek","doi":"10.1007/s10618-024-01056-5","DOIUrl":"https://doi.org/10.1007/s10618-024-01056-5","url":null,"abstract":"An outlier probability is the probability that an observation is an outlier. Typically, outlier detection algorithms calculate real-valued outlier scores to identify outliers. Converting outlier scores into outlier probabilities increases the interpretability of outlier scores for domain experts and makes outlier scores from different outlier detection algorithms comparable. Although several transformations to convert outlier scores to outlier probabilities have been proposed in the literature, there is no common understanding of good outlier probabilities and no standard approach to evaluate outlier probabilities. We require that good outlier probabilities be sharp, refined, and calibrated. To evaluate these properties, we adapt and propose novel measures that use ground-truth labels indicating which observation is an outlier or an inlier. The refinement and calibration measures partition the outlier probabilities into bins or use kernel smoothing. Compared to the evaluation of probability in supervised learning, several aspects are relevant when evaluating outlier probabilities, mainly due to the imbalanced and often unsupervised nature of outlier detection. First, stratified and weighted measures are necessary to evaluate the probabilities of outliers well. Second, the joint use of the sharpness, refinement, and calibration errors makes it possible to independently measure the corresponding characteristics of outlier probabilities. Third, equiareal bins, where the product of observations per bin times bin length is constant, balance the number of observations per bin and bin length, allowing accurate evaluation of different outlier probability ranges. Finally, we show that good outlier probabilities, according to the proposed measures, improve the performance of the follow-up task of converting outlier probabilities into labels for outliers and inliers.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739956","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