Data Mining and Knowledge Discovery最新文献_第4页

GeoRF: a geospatial random forest GeoRF：地理空间随机森林

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-06-19 DOI: 10.1007/s10618-024-01046-7

Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt

{"title":"GeoRF: a geospatial random forest","authors":"Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt","doi":"10.1007/s10618-024-01046-7","DOIUrl":"https://doi.org/10.1007/s10618-024-01046-7","url":null,"abstract":"The geospatial domain increasingly relies on data-driven methodologies to extract actionable insights from the growing volume of available data. Despite the effectiveness of tree-based models in capturing complex relationships between features and targets, they fall short when it comes to considering spatial factors. This limitation arises from their reliance on univariate, axis-parallel splits that result in rectangular areas on a map. To address this issue and enhance both performance and interpretability, we propose a solution that introduces two novel bivariate splits: an oblique and Gaussian split designed specifically for geographic coordinates. Our innovation, called Geospatial Random Forest (geoRF), builds upon Geospatial Regression Trees (GeoTrees) to effectively incorporate geographic features and extract maximum spatial insights. Through an extensive benchmark, we show that our geoRF model outperforms traditional spatial statistical models, other spatial RF variations, machine learning and deep learning methods across a range of geospatial tasks. Furthermore, we contextualize our method’s computational time complexity relative to baseline approaches. Our prediction maps illustrate that geoRF produces more robust and intuitive decision boundaries compared to conventional tree-based models. Utilizing impurity-based feature importance measures, we validate geoRF’s effectiveness in highlighting the significance of geographic coordinates, especially in data sets exhibiting pronounced spatial patterns.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"22 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528877","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

Modelling event sequence data by type-wise neural point process 用类型神经点过程对事件序列数据建模

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-06-17 DOI: 10.1007/s10618-024-01047-6

Bingqing Liu

{"title":"Modelling event sequence data by type-wise neural point process","authors":"Bingqing Liu","doi":"10.1007/s10618-024-01047-6","DOIUrl":"https://doi.org/10.1007/s10618-024-01047-6","url":null,"abstract":"Event sequence data widely exists in real life, where each event is typically represented as a tuple, event type and occurrence time. Recently, neural point process (NPP), a probabilistic model that learns the next event distribution with events history given, has gained a lot of attention for event sequence modelling. Existing NPP models use one single vector to encode the whole events history. However, each type of event has its own historical events of concern, which should have led to a different encoding for events history. To this end, we propose Type-wise Neural Point Process (TNPP), with each type of event having a history vector to encode the historical events of its own interest. Type-wise encoding further leads to the realization of type-wise decoding, which together makes a more effective neural point process. Experimental results on six datasets show that TNPP outperforms existing models on the event type prediction task under both extrapolation and interpolation setting. Moreover, the results in terms of scalability and interpretability show that TNPP scales well to datasets with many event types and can provide high-quality event dependencies for interpretation. The code and data can be found at https://github.com/lbq8942/TNPP.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528876","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

The impact of variable ordering on Bayesian network structure learning 变量排序对贝叶斯网络结构学习的影响

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-06-08 DOI: 10.1007/s10618-024-01044-9

Neville K. Kitson, Anthony C. Constantinou

{"title":"The impact of variable ordering on Bayesian network structure learning","authors":"Neville K. Kitson, Anthony C. Constantinou","doi":"10.1007/s10618-024-01044-9","DOIUrl":"https://doi.org/10.1007/s10618-024-01044-9","url":null,"abstract":"Causal Bayesian Networks (CBNs) provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are becoming increasingly important. In the literature, the validity of these algorithms is often tested for sensitivity over varying sample sizes, hyper-parameters, and occasionally objective functions, but the effect of the order in which the variables are read from data is rarely quantified. We show that many commonly-used algorithms, both established and state-of-the-art, are more sensitive to variable ordering than these other factors when learning CBNs from discrete variables. This effect is strongest in hill-climbing and its variants where we explain how it arises, but extends to hybrid, and to a lesser-extent, constraint-based algorithms. Because the variable ordering is arbitrary, any significant effect it has on learnt graph accuracy is concerning, and raises questions about the validity of both many older and more recent results produced by these algorithms in practical applications and their rankings in performance evaluations.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"44 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503031","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

Uplift modeling with quasi-loss-functions 用准损耗函数进行上浮建模

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-06-04 DOI: 10.1007/s10618-024-01042-x

Jinping Hu, Evert de Haan, Bernd Skiera

引用次数: 0

Modeling the impact of out-of-schema questions in task-oriented dialog systems 面向任务的对话系统中模式外问题的影响建模

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-06-04 DOI: 10.1007/s10618-024-01039-6

Jannat Ara Meem, Muhammad Shihab Rashid, Vagelis Hristidis

{"title":"Modeling the impact of out-of-schema questions in task-oriented dialog systems","authors":"Jannat Ara Meem, Muhammad Shihab Rashid, Vagelis Hristidis","doi":"10.1007/s10618-024-01039-6","DOIUrl":"https://doi.org/10.1007/s10618-024-01039-6","url":null,"abstract":"Existing work on task-oriented dialog systems generally assumes that the interaction of users with the system is restricted to the information stored in a closed data schema. However, in practice users may ask ‘out-of-schema’ questions, that is, questions that the system cannot answer, because the information does not exist in the schema. Failure to answer these questions may lead the users to drop out of the chat before reaching the success state (e.g. reserving a restaurant). A key challenge is that the number of these questions may be too high for a domain expert to answer them all. We formulate the problem of out-of-schema question detection and selection that identifies the most critical out-of-schema questions to answer, in order to maximize the expected success rate of the system. We propose a two-stage pipeline to solve the problem. In the first stage, we propose a novel in-context learning (ICL) approach to detect out-of-schema questions. In the second stage, we propose two algorithms for out-of-schema question selection (OQS): a naive approach that chooses a question based on its frequency in the dropped-out conversations, and a probabilistic approach that represents each conversation as a Markov chain and a question is picked based on its overall benefit. We propose and publish two new datasets for the problem, as existing datasets do not contain out-of-schema questions or user drop-outs. Our quantitative and simulation-based experimental analyses on these datasets measure how our methods can effectively identify out-of-schema questions and positively impact the success rate of the system.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"43 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141258149","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

Improving graph-based recommendation with unraveled graph learning 利用未揭示图学习改进基于图的推荐

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-06-02 DOI: 10.1007/s10618-024-01038-7

Chih-Chieh Chang, Diing-Ruey Tzeng, Chia-Hsun Lu, Ming-Yi Chang, Chih-Ya Shen

{"title":"Improving graph-based recommendation with unraveled graph learning","authors":"Chih-Chieh Chang, Diing-Ruey Tzeng, Chia-Hsun Lu, Ming-Yi Chang, Chih-Ya Shen","doi":"10.1007/s10618-024-01038-7","DOIUrl":"https://doi.org/10.1007/s10618-024-01038-7","url":null,"abstract":"Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph Neural Networks. Furthermore, the integration of contrastive learning has enhanced the performance of GraphCF methods. Recent research has shifted from graph augmentation to noise perturbation in contrastive learning, leading to significant performance improvements. However, we contend that the primary factor in performance enhancement is not graph augmentation or noise perturbation, but rather the balance of the embedding from each layer in the output embedding. To substantiate our claim, we conducted preliminary experiments with multiple state-of-the-art GraphCF methods. Based on our observations and insights, we propose a novel approach named Unraveled Graph Contrastive Learning (UGCL), which includes a new propagation scheme to further enhance performance. To the best of our knowledge, this is the first approach that specifically addresses the balance factor in the output embedding for performance improvement. We have carried out extensive experiments on multiple large-scale benchmark datasets to evaluate the effectiveness of our proposed approach. The results indicate that UGCL significantly outperforms all other state-of-the-art baseline models, also showing superior performance in terms of fairness and debiasing capabilities compared to other baselines.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259415","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

A practical approach to novel class discovery in tabular data 在表格数据中发现新类别的实用方法

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-31 DOI: 10.1007/s10618-024-01025-y

Troisemaine Colin, Reiffers-Masson Alexandre, Gosselin Stéphane, Lemaire Vincent, Vaton Sandrine

{"title":"A practical approach to novel class discovery in tabular data","authors":"Troisemaine Colin, Reiffers-Masson Alexandre, Gosselin Stéphane, Lemaire Vincent, Vaton Sandrine","doi":"10.1007/s10618-024-01025-y","DOIUrl":"https://doi.org/10.1007/s10618-024-01025-y","url":null,"abstract":"The problem of novel class discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled set of novel classes. While NCD has recently received a lot of attention from the community, it is often solved on computer vision problems and under unrealistic conditions. In particular, the number of novel classes is usually assumed to be known in advance, and their labels are sometimes used to tune hyperparameters. Methods that rely on these assumptions are not applicable in real-world scenarios. In this work, we focus on solving NCD in tabular data when no prior knowledge of the novel classes is available. To this end, we propose to tune the hyperparameters of NCD methods by adapting the k-fold cross-validation process and hiding some of the known classes in each fold. Since we have found that methods with too many hyperparameters are likely to overfit these hidden classes, we define a simple deep NCD model. This method is composed of only the essential elements necessary for the NCD problem and shows robust performance under realistic conditions. Furthermore, we find that the latent space of this method can be used to reliably estimate the number of novel classes. Additionally, we adapt two unsupervised clustering algorithms (k-means and Spectral Clustering) to leverage the knowledge of the known classes. Extensive experiments are conducted on 7 tabular datasets and demonstrate the effectiveness of the proposed method and hyperparameter tuning process, and show that the NCD problem can be solved without relying on knowledge from the novel classes.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"123 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197835","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

Bias-aware ranking from pairwise comparisons 通过成对比较进行有偏差的排序

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-31 DOI: 10.1007/s10618-024-01024-z

Antonio Ferrara, Francesco Bonchi, Francesco Fabbri, Fariba Karimi, Claudia Wagner

{"title":"Bias-aware ranking from pairwise comparisons","authors":"Antonio Ferrara, Francesco Bonchi, Francesco Fabbri, Fariba Karimi, Claudia Wagner","doi":"10.1007/s10618-024-01024-z","DOIUrl":"https://doi.org/10.1007/s10618-024-01024-z","url":null,"abstract":"Human feedback is often used, either directly or indirectly, as input to algorithmic decision making. However, humans are biased: if the algorithm that takes as input the human feedback does not control for potential biases, this might result in biased algorithmic decision making, which can have a tangible impact on people’s lives. In this paper, we study how to detect and correct for evaluators’ bias in the task of ranking people (or items) from pairwise comparisons. Specifically, we assume we are given pairwise comparisons of the items to be ranked produced by a set of evaluators. While the pairwise assessments of the evaluators should reflect to a certain extent the latent (unobservable) true quality scores of the items, they might be affected by each evaluator’s own bias against, or in favor, of some groups of items. By detecting and amending evaluators’ biases, we aim to produce a ranking of the items that is, as much as possible, in accordance with the ranking one would produce by having access to the latent quality scores. Our proposal is a novel method that extends the classic Bradley-Terry model by having a bias parameter for each evaluator which distorts the true quality score of each item, depending on the group the item belongs to. Thanks to the simplicity of the model, we are able to write explicitly its log-likelihood w.r.t. the parameters (i.e., items’ latent scores and evaluators’ bias) and optimize by means of the alternating approach. Our experiments on synthetic and real-world data confirm that our method is able to reconstruct the bias of each single evaluator extremely well and thus to outperform several non-trivial competitors in the task of producing a ranking which is as much as possible close to the unbiased ranking.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"5 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197974","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

LoCoMotif: discovering time-warped motifs in time series LoCoMotif：发现时间序列中的时间扭曲图案

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-30 DOI: 10.1007/s10618-024-01032-z

Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel

引用次数: 0

On the impact of multi-dimensional local differential privacy on fairness 多维局部差异隐私对公平性的影响

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2024-05-27 DOI: 10.1007/s10618-024-01031-0

Karima Makhlouf, Héber H. Arcolezi, Sami Zhioua, Ghassen Ben Brahim, Catuscia Palamidessi

{"title":"On the impact of multi-dimensional local differential privacy on fairness","authors":"Karima Makhlouf, Héber H. Arcolezi, Sami Zhioua, Ghassen Ben Brahim, Catuscia Palamidessi","doi":"10.1007/s10618-024-01031-0","DOIUrl":"https://doi.org/10.1007/s10618-024-01031-0","url":null,"abstract":"Automated decision systems are increasingly used to make consequential decisions in people’s lives. Due to the sensitivity of the manipulated data and the resulting decisions, several ethical concerns need to be addressed for the appropriate use of such technologies, particularly fairness and privacy. Unlike previous work, which focused on centralized differential privacy (DP) or on local DP (LDP) for a single sensitive attribute, in this paper, we examine the impact of LDP in the presence of several sensitive attributes (i.e., multi-dimensional data) on fairness. Detailed empirical analysis on synthetic and benchmark datasets revealed very relevant observations. In particular, (1) multi-dimensional LDP is an efficient approach to reduce disparity, (2) the variant of the multi-dimensional approach of LDP (we employ two variants) matters only at low privacy guarantees (high (epsilon)), and (3) the true decision distribution has an important effect on which group is more sensitive to the obfuscation. Last, we summarize our findings in the form of recommendations to guide practitioners in adopting effective privacy-preserving practices while maintaining fairness and utility in machine learning applications.","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"37 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172109","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