Advances in Data Analysis and Classification最新文献_第9页

Spatial quantile clustering of climate data 气候数据的空间量化聚类

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-02-22 DOI: 10.1007/s11634-024-00580-y

Carlo Gaetan, Paolo Girardi, Victor Muthama Musau

引用次数: 0

Robust functional logistic regression 稳健功能逻辑回归

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-02-12 DOI: 10.1007/s11634-023-00577-z

Berkay Akturk, Ufuk Beyaztas, Han Lin Shang, Abhijit Mandal

{"title":"Robust functional logistic regression","authors":"Berkay Akturk, Ufuk Beyaztas, Han Lin Shang, Abhijit Mandal","doi":"10.1007/s11634-023-00577-z","DOIUrl":"10.1007/s11634-023-00577-z","url":null,"abstract":"<div><p>Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robust estimation procedure for functional logistic regression, in which the observations of the functional predictor are projected onto a set of finite-dimensional subspaces via robust functional principal component analysis. This dimension-reduction step reduces the outlying effects in the functional predictor. The logistic regression coefficient is estimated using an M-type estimator based on binary response and robust principal component scores. In doing so, we provide robust estimates by minimizing the effects of outliers in the binary response and functional predictor variables. Via a series of Monte-Carlo simulations and using hand radiograph data, we examine the parameter estimation and classification accuracy for the response variable. We find that the robust procedure outperforms some existing robust and non-robust methods when outliers are present, while producing competitive results when outliers are absent. In addition, the proposed method is computationally more efficient than some existing robust alternatives.\u0000</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 1","pages":"121 - 145"},"PeriodicalIF":1.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-023-00577-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139771456","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

Neural networks with functional inputs for multi-class supervised classification of replicated point patterns 用于复制点模式多类监督分类的功能输入神经网络

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-02-07 DOI: 10.1007/s11634-024-00579-5

Kateřina Pawlasová, Iva Karafiátová, Jiří Dvořák

{"title":"Neural networks with functional inputs for multi-class supervised classification of replicated point patterns","authors":"Kateřina Pawlasová, Iva Karafiátová, Jiří Dvořák","doi":"10.1007/s11634-024-00579-5","DOIUrl":"10.1007/s11634-024-00579-5","url":null,"abstract":"<div><p>A spatial point pattern is a collection of points observed in a bounded region of the Euclidean plane or space. With the dynamic development of modern imaging methods, large datasets of point patterns are available representing for example sub-cellular location patterns for human proteins or large forest populations. The main goal of this paper is to show the possibility of solving the supervised multi-class classification task for this particular type of complex data via functional neural networks. To predict the class membership for a newly observed point pattern, we compute an empirical estimate of a selected functional characteristic. Then, we consider such estimated function to be a functional variable entering the network. In a simulation study, we show that the neural network approach outperforms the kernel regression classifier that we consider a benchmark method in the point pattern setting. We also analyse a real dataset of point patterns of intramembranous particles and illustrate the practical applicability of the proposed method.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 3","pages":"705 - 721"},"PeriodicalIF":1.4,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00579-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139771644","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

k-means clustering for persistent homology 针对持久同源性的 k-means 聚类方法

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-01-31 DOI: 10.1007/s11634-023-00578-y

Yueqi Cao, Prudence Leung, Anthea Monod

引用次数: 0

RGA: a unified measure of predictive accuracy RGA：预测准确性的统一衡量标准

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-01-17 DOI: 10.1007/s11634-023-00574-2

Paolo Giudici, Emanuela Raffinetti

引用次数: 0

QDA classification of high-dimensional data with rare and weak signals 对具有稀有和微弱信号的高维数据进行 QDA 分类

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-12-18 DOI: 10.1007/s11634-023-00576-0

Hanning Chen, Qiang Zhao, Jingjing Wu

{"title":"QDA classification of high-dimensional data with rare and weak signals","authors":"Hanning Chen, Qiang Zhao, Jingjing Wu","doi":"10.1007/s11634-023-00576-0","DOIUrl":"10.1007/s11634-023-00576-0","url":null,"abstract":"<div><p>This paper addresses the two-class classification problem for data with rare and weak signals, under the modern high-dimension setup <span>(p>>n)</span>. Considering the two-component mixture of Gaussian features with different random mean vector of rare and weak signals but common covariance matrix (homoscedastic Gaussian), Fan (AS 41:2537-2571, 2013) investigated the optimality of linear discriminant analysis (LDA) and proposed an efficient variable selection and classification procedure. We extend their work by incorporating the more general scenario that the two components have different random covariance matrices with difference of rare and weak signals, in order to assess the effect of difference in covariance matrix on classification. Under this model, we investigated the behaviour of quadratic discriminant analysis (QDA) classifier. In theoretical aspect, we derived the successful and unsuccessful classification regions of QDA. For data of rare signals, variable selection will mostly improve the performance of statistical procedures. Thus in implementation aspect, we proposed a variable selection procedure for QDA based on the Higher Criticism Thresholding (HCT) that was proved efficient for LDA. In addition, we conducted extensive simulation studies to demonstrate the successful and unsuccessful classification regions of QDA and evaluate the effectiveness of the proposed HCT thresholded QDA.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 1","pages":"31 - 65"},"PeriodicalIF":1.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-023-00576-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138745929","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

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

IF 1.4 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":"10.1007/s11634-023-00573-3","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 1","pages":"5 - 30"},"PeriodicalIF":1.4,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-023-00573-3.pdf","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":"OA","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 1.4 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":"10.1007/s11634-023-00575-1","url":null,"abstract":"<div><p>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 <i>mean shift procedure</i>, 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.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 4","pages":"1067 - 1095"},"PeriodicalIF":1.4,"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 1.3 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><p>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 <span>Statlog</span> dataset, a large collection of satellite imaging spectra, to search for novel soil types.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 3","pages":"681 - 703"},"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