Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate

• URL: http://arxiv.org/abs/2205.07833v1
• Date: Mon, 16 May 2022 17:43:35 GMT
• Title: Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate
• Authors: Yuting Ye, Christine Ho, Ci-Ren Jiang, Wayne Tai Lee, Haiyan Huang
• Abstract summary: We introduce a new statistic called the multidimensional local precision rate (mLPR) for each object in each class. We show that classification decisions made by simply sorting objects across classes in descending order of their mLPRs can, in theory, ensure the class hierarchy. In response, we introduce HierRank, a new algorithm that maximizes an empirical version of CATCH using estimated mLPRs while respecting the hierarchy.
• Score: 4.812468844362369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hierarchical multi-label classification (HMC) has drawn increasing attention in the past few decades. It is applicable when hierarchical relationships among classes are available and need to be incorporated along with the multi-label classification whereby each object is assigned to one or more classes. There are two key challenges in HMC: i) optimizing the classification accuracy, and meanwhile ii) ensuring the given class hierarchy. To address these challenges, in this article, we introduce a new statistic called the multidimensional local precision rate (mLPR) for each object in each class. We show that classification decisions made by simply sorting objects across classes in descending order of their true mLPRs can, in theory, ensure the class hierarchy and lead to the maximization of CATCH, an objective function we introduce that is related to the area under a hit curve. This approach is the first of its kind that handles both challenges in one objective function without additional constraints, thanks to the desirable statistical properties of CATCH and mLPR. In practice, however, true mLPRs are not available. In response, we introduce HierRank, a new algorithm that maximizes an empirical version of CATCH using estimated mLPRs while respecting the hierarchy. The performance of this approach was evaluated on a synthetic data set and two real data sets; ours was found to be superior to several comparison methods on evaluation criteria based on metrics such as precision, recall, and $F_1$ score.

Related papers

• Generating Hierarchical Structures for Improved Time Series Classification Using Stochastic Splitting Functions [0.0]
  This study introduces a novel hierarchical divisive clustering approach with splitting functions (SSFs) to enhance classification performance in multi-class datasets through hierarchical classification (HC) The method has the unique capability of generating hierarchy without requiring explicit information, making it suitable for datasets lacking prior knowledge of hierarchy.
  arXiv  Detail & Related papers  (2023-09-21T10:34:50Z)
• Enhancing Classification with Hierarchical Scalable Query on Fusion Transformer [0.4129225533930965]
  This paper proposes a method to boost fine-grained classification through a hierarchical approach via learnable independent query embeddings. We exploit the idea of hierarchy to learn query embeddings that are scalable across all levels. Our method is able to outperform the existing methods with an improvement of 11% at the fine-grained classification.
  arXiv  Detail & Related papers  (2023-02-28T11:00:55Z)
• Task-Specific Embeddings for Ante-Hoc Explainable Text Classification [6.671252951387647]
  We propose an alternative training objective in which we learn task-specific embeddings of text. Our proposed objective learns embeddings such that all texts that share the same target class label should be close together. We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy.
  arXiv  Detail & Related papers  (2022-11-30T19:56:25Z)
• Class-Incremental Lifelong Learning in Multi-Label Classification [3.711485819097916]
  This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN) with a built Augmented Correlation Matrix (ACM) across sequential partial-label tasks.
  arXiv  Detail & Related papers  (2022-07-16T05:14:07Z)
• The Overlooked Classifier in Human-Object Interaction Recognition [82.20671129356037]
  We encode the semantic correlation among classes into the classification head by initializing the weights with language embeddings of HOIs. We propose a new loss named LSE-Sign to enhance multi-label learning on a long-tailed dataset. Our simple yet effective method enables detection-free HOI classification, outperforming the state-of-the-arts that require object detection and human pose by a clear margin.
  arXiv  Detail & Related papers  (2022-03-10T23:35:00Z)
• Rank4Class: A Ranking Formulation for Multiclass Classification [26.47229268790206]
  Multiclass classification (MCC) is a fundamental machine learning problem. We show that it is easy to boost MCC performance with a novel formulation through the lens of ranking.
  arXiv  Detail & Related papers  (2021-12-17T19:22:37Z)
• PLM: Partial Label Masking for Imbalanced Multi-label Classification [59.68444804243782]
  Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.
  arXiv  Detail & Related papers  (2021-05-22T18:07:56Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Self-Weighted Robust LDA for Multiclass Classification with Edge Classes [111.5515086563592]
  A novel self-weighted robust LDA with l21-norm based between-class distance criterion, called SWRLDA, is proposed for multi-class classification. The proposed SWRLDA is easy to implement, and converges fast in practice.
  arXiv  Detail & Related papers  (2020-09-24T12:32:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.