Advances in Data Analysis and Classification最新文献_第4页

Loss-guided stability selection 损失引导的稳定性选择

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-12-15 DOI: 10.1007/s11634-023-00573-3

Tino Werner

{"title":"Loss-guided stability selection","authors":"Tino Werner","doi":"10.1007/s11634-023-00573-3","DOIUrl":"https://doi.org/10.1007/s11634-023-00573-3","url":null,"abstract":"In modern data analysis, sparse model selection becomes inevitable once the number of predictor variables is very high. It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. The celebrated Stability Selection overcomes these weaknesses by aggregating models, based on subsamples of the training data, followed by choosing a stable predictor set which is usually much sparser than the predictor sets from the raw models. The standard Stability Selection is based on a global criterion, namely the per-family error rate, while additionally requiring expert knowledge to suitably configure the hyperparameters. Model selection depends on the loss function, i.e., predictor sets selected w.r.t. some particular loss function differ from those selected w.r.t. some other loss function. Therefore, we propose a Stability Selection variant which respects the chosen loss function via an additional validation step based on out-of-sample validation data, optionally enhanced with an exhaustive search strategy. Our Stability Selection variants are widely applicable and user-friendly. Moreover, our Stability Selection variants can avoid the issue of severe underfitting, which affects the original Stability Selection for noisy high-dimensional data, so our priority is not to avoid false positives at all costs but to result in a sparse stable model with which one can make predictions. Experiments where we consider both regression and binary classification with Boosting as model selection algorithm reveal a significant precision improvement compared to raw Boosting models while not suffering from any of the mentioned issues of the original Stability Selection.","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138690803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 fresh look at mean-shift based modal clustering 重新审视基于均值移动的模态聚类

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-12-14 DOI: 10.1007/s11634-023-00575-1

Jose Ameijeiras-Alonso, Jochen Einbeck

{"title":"A fresh look at mean-shift based modal clustering","authors":"Jose Ameijeiras-Alonso, Jochen Einbeck","doi":"10.1007/s11634-023-00575-1","DOIUrl":"https://doi.org/10.1007/s11634-023-00575-1","url":null,"abstract":"Modal clustering is an unsupervised learning technique where cluster centers are identified as the local maxima of nonparametric probability density estimates. A natural algorithmic engine for the computation of these maxima is the mean shift procedure, which is essentially an iteratively computed chain of local means. We revisit this technique, focusing on its link to kernel density gradient estimation, in this course proposing a novel concept for bandwidth selection based on the concept of a critical bandwidth. Furthermore, in the one-dimensional case, an inverse version of the mean shift is developed to provide a novel approach for the estimation of antimodes, which is then used to identify cluster boundaries. A simulation study is provided which assesses, in the univariate case, the classification accuracy of the mean-shift based clustering approach. Three (univariate and multivariate) examples from the fields of philately, engineering, and imaging, illustrate how modal clusterings identified through mean shift based methods relate directly and naturally to physical properties of the data-generating system. Solutions are proposed to deal computationally efficiently with large data sets.","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138690553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 probabilistic method for reconstructing the Foreign Direct Investments network in search of ultimate host economies 重构外国直接投资网络以寻找最终东道国经济的概率方法

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-12-08 DOI: 10.1007/s11634-023-00571-5

Nadia Accoto, Valerio Astuti, Costanza Catalano

引用次数: 0

Variational inference for semiparametric Bayesian novelty detection in large datasets 大数据集中半参数贝叶斯新颖性检测的变分推理

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-12-04 DOI: 10.1007/s11634-023-00569-z

Luca Benedetti, Eric Boniardi, Leonardo Chiani, Jacopo Ghirri, Marta Mastropietro, Andrea Cappozzo, Francesco Denti

{"title":"Variational inference for semiparametric Bayesian novelty detection in large datasets","authors":"Luca Benedetti, Eric Boniardi, Leonardo Chiani, Jacopo Ghirri, Marta Mastropietro, Andrea Cappozzo, Francesco Denti","doi":"10.1007/s11634-023-00569-z","DOIUrl":"10.1007/s11634-023-00569-z","url":null,"abstract":"<div>After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instances of an unlabeled test set while allowing for the presence of previously unseen classes. These models are valuable in many areas, ranging from social network and food adulteration analyses to biology, where an evolving population may be present. In this paper, we focus on a two-stage Bayesian semiparametric novelty detector, also known as Brand, recently introduced in the literature. Leveraging on a model-based mixture representation, Brand allows clustering the test observations into known training terms or a single novelty term. Furthermore, the novelty term is modeled with a Dirichlet Process mixture model to flexibly capture any departure from the known patterns. Brand was originally estimated using MCMC schemes, which are prohibitively costly when applied to high-dimensional data. To scale up Brand applicability to large datasets, we propose to resort to a variational Bayes approach, providing an efficient algorithm for posterior approximation. We demonstrate a significant gain in efficiency and excellent classification performance with thorough simulation studies. Finally, to showcase its applicability, we perform a novelty detection analysis using the openly-available Statlog dataset, a large collection of satellite imaging spectra, to search for novel soil types.</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-023-00569-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138505015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Claims fraud detection with uncertain labels 标签不确定的索赔欺诈检测

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-11-30 DOI: 10.1007/s11634-023-00568-0

Félix Vandervorst, Wouter Verbeke, Tim Verdonck

引用次数: 0

Robust and sparse logistic regression 鲁棒稀疏逻辑回归

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-11-27 DOI: 10.1007/s11634-023-00572-4

Dries Cornilly, Lise Tubex, Stefan Van Aelst, Tim Verdonck

引用次数: 0

Semiparametric mixture of linear regressions with nonparametric Gaussian scale mixture errors 具有非参数高斯尺度混合误差的半参数混合线性回归

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-11-23 DOI: 10.1007/s11634-023-00570-6

Sangkon Oh, Byungtae Seo

引用次数: 0

Functional clustering of fictional narratives using Vonnegut curves 利用冯内古特曲线的虚构叙事功能聚类

4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-11-04 DOI: 10.1007/s11634-023-00567-1

Shan Zhong, David B. Hitchcock

引用次数: 0

A between-cluster approach for clustering skew-symmetric data 斜对称数据聚类的聚类间方法

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-10-28 DOI: 10.1007/s11634-023-00566-2

Donatella Vicari, Cinzia Di Nuzzo

引用次数: 0

Applications of dual regularized Laplacian matrix for community detection 对偶正则拉普拉斯矩阵在社区检测中的应用

4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-10-26 DOI: 10.1007/s11634-023-00565-3

Huan Qing, Jingli Wang

引用次数: 0