Journal of Classification最新文献

How to Measure the Researcher Impact with the Aid of its Impactable Area: A Concrete Approach Using Distance Geometry 如何借助可影响区域测量研究人员的影响？利用距离几何学的具体方法

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-08-26 DOI: 10.1007/s00357-024-09490-2

Beniamino Cappelletti-Montano, Gianmarco Cherchi, Benedetto Manca, Stefano Montaldo, Monica Musio

引用次数: 0

Multi-task Support Vector Machine Classifier with Generalized Huber Loss 具有广义休伯损失的多任务支持向量机分类器

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-08-23 DOI: 10.1007/s00357-024-09488-w

Qi Liu, Wenxin Zhu, Zhengming Dai, Zhihong Ma

{"title":"Multi-task Support Vector Machine Classifier with Generalized Huber Loss","authors":"Qi Liu, Wenxin Zhu, Zhengming Dai, Zhihong Ma","doi":"10.1007/s00357-024-09488-w","DOIUrl":"https://doi.org/10.1007/s00357-024-09488-w","url":null,"abstract":"Compared to single-task learning (STL), multi-task learning (MTL) achieves a better generalization by exploiting domain-specific information implicit in the training signals of several related tasks. The adaptation of MTL to support vector machines (SVMs) is a rather successful example. Inspired by the recently published generalized Huber loss SVM (GHSVM) and regularized multi-task learning (RMTL), we propose a novel generalized Huber loss multi-task support vector machine including linear and non-linear cases for binary classification, named as MTL-GHSVM. The new method extends the GHSVM from single-task to multi-task learning, and the application of Huber loss to MTL-SVM is innovative to the best of our knowledge. The proposed method has two main advantages: on the one hand, compared with SVMs with hinge loss and GHSVM, our MTL-GHSVM using the differentiable generalized Huber loss has better generalization performance; on the other hand, it adopts functional iteration to find the optimal solution, and does not need to solve a quadratic programming problem (QPP), which can significantly reduce the computational cost. Numerical experiments have been conducted on fifteen real datasets, and the results demonstrate the effectiveness of the proposed multi-task classification algorithm compared with the state-of-the-art algorithms.","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218986","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

Clustering-Based Oversampling Algorithm for Multi-class Imbalance Learning 基于聚类的多类失衡学习过采样算法

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-08-22 DOI: 10.1007/s00357-024-09491-1

Haixia Zhao, Jian Wu

引用次数: 0

Combining Semi-supervised Clustering and Classification Under a Generalized Framework 通用框架下的半监督聚类与分类相结合

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-08-13 DOI: 10.1007/s00357-024-09489-9

Zhen Jiang, Lingyun Zhao, Yu Lu

{"title":"Combining Semi-supervised Clustering and Classification Under a Generalized Framework","authors":"Zhen Jiang, Lingyun Zhao, Yu Lu","doi":"10.1007/s00357-024-09489-9","DOIUrl":"https://doi.org/10.1007/s00357-024-09489-9","url":null,"abstract":"Most machine learning algorithms rely on having a sufficient amount of labeled data to train a reliable classifier. However, labeling data is often costly and time-consuming, while unlabeled data can be readily accessible. Therefore, learning from both labeled and unlabeled data has become a hot topic of interest. Inspired by the co-training algorithm, we present a learning framework called CSCC, which combines semi-supervised clustering and classification to learn from both labeled and unlabeled data. Unlike existing co-training style methods that construct diverse classifiers to learn from each other, CSCC leverages the diversity between semi-supervised clustering and classification models to achieve mutual enhancement. Existing classification algorithms can be easily adapted to CSCC, allowing them to generalize from a few labeled data. Especially, in order to bridge the gap between class information and clustering, we propose a semi-supervised hierarchical clustering algorithm that utilizes labeled data to guide the process of cluster-splitting. Within the CSCC framework, we introduce two loss functions to supervise the iterative updating of the semi-supervised clustering and classification models, respectively. Extensive experiments conducted on a variety of benchmark datasets validate the superiority of CSCC over other state-of-the-art methods.","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218987","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

Slope Stability Classification Model Based on Single-Valued Neutrosophic Matrix Energy and Its Application Under a Single-Valued Neutrosophic Matrix Scenario 基于单值中性矩阵能量的边坡稳定性分类模型及其在单值中性矩阵情景下的应用

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-07-23 DOI: 10.1007/s00357-024-09487-x

Jun Ye, Kaiqian Du, Shigui Du, Rui Yong

{"title":"Slope Stability Classification Model Based on Single-Valued Neutrosophic Matrix Energy and Its Application Under a Single-Valued Neutrosophic Matrix Scenario","authors":"Jun Ye, Kaiqian Du, Shigui Du, Rui Yong","doi":"10.1007/s00357-024-09487-x","DOIUrl":"https://doi.org/10.1007/s00357-024-09487-x","url":null,"abstract":"Since matrix energy (ME) implies the expressive merit of collective information, a classification method based on ME has not been investigated in the existing literature, which reflects its research gap in a matrix scenario. Therefore, the purpose of this paper is to propose a slope stability classification model based on the single-valued neutrosophic matrix (SVNM) energy to solve the current research gap in slope stability classification analysis with uncertain and inconsistent information. In this study, we first present SVNM and define the SVNM energy based on true, uncertain, and false MEs. Then, using a neutrosophication technique based on true, false, and uncertain Gaussian membership functions, the multiple sampling data of the stability affecting factors for each slope are transformed into SVNM. Next, a slope stability classification model based on the SVNM energy and score function is developed to solve the slope stability classification analysis under the full SVNM scenario of both the affecting factor weights and the affecting factors of slope stability. Finally, the developed classification model is applied to the classification analysis of 50 slope samples collected from different areas of Zhejiang province in China as a case study to verify its rationality and accuracy under the SVNM scenario. The accuracy of the classification results for the 50 slope samples is 100%.","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782264","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

An Effective Crow Search Algorithm and Its Application in Data Clustering 一种有效的乌鸦搜索算法及其在数据聚类中的应用

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-07-23 DOI: 10.1007/s00357-024-09486-y

Rajesh Ranjan, Jitender Kumar Chhabra

引用次数: 0

Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions 使用广义双曲分布的稀疏混合物进行灵活聚类

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-07-12 DOI: 10.1007/s00357-024-09479-x

Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang, Paul D. McNicholas

引用次数: 0

Clustering with Minimum Spanning Trees: How Good Can It Be? 用最小生成树进行聚类：它能有多好？

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-07-06 DOI: 10.1007/s00357-024-09483-1

Marek Gagolewski, Anna Cena, Maciej Bartoszuk, Łukasz Brzozowski

引用次数: 0

A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction 机器学习模型中用于特定克雷洛夫求解器预测的新矩阵特征选择策略

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-07-06 DOI: 10.1007/s00357-024-09484-0

Hai-Bing Sun, Yan-Fei Jing, Xiao-Wen Xu

{"title":"A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction","authors":"Hai-Bing Sun, Yan-Fei Jing, Xiao-Wen Xu","doi":"10.1007/s00357-024-09484-0","DOIUrl":"https://doi.org/10.1007/s00357-024-09484-0","url":null,"abstract":"Numerical simulation processes in scientific and engineering applications require efficient solutions of large sparse linear systems, and variants of Krylov subspace solvers with various preconditioning techniques have been developed. However, it is time-consuming for practitioners with trial and error to find a high-performance Krylov solver in a candidate solver set for a given linear system. Therefore, it is instructive to select an efficient solver intelligently among a solver set rather than exploratory application of all solvers to solve the linear system. One promising direction of solver selection is to apply machine learning methods to construct a mapping from the matrix features to the candidate solvers. However, the computation of some matrix features is quite difficult. In this paper, we design a new selection strategy of matrix features to reduce computing cost, and then employ the selected features to construct a machine learning classifier to predict an appropriate solver for a given linear system. Numerical experiments on two attractive GMRES-type solvers for solving linear systems from the University of Florida Sparse Matrix Collection and Matrix Market verify the efficiency of our strategy, not only reducing the computing time for obtaining features and construction time of classifier but also keeping more than 90% prediction accuracy.","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573774","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

Cluster Validation Based on Fisher’s Linear Discriminant Analysis 基于费雪线性判别分析的聚类验证

IF 2 4区计算机科学

Journal of Classification Pub Date : 2024-07-04 DOI: 10.1007/s00357-024-09481-3

Fabian Kächele, Nora Schneider

引用次数: 0