A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification

• URL: http://arxiv.org/abs/2204.06138v1
• Date: Wed, 13 Apr 2022 02:10:14 GMT
• Title: A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification
• Authors: Gao Pengfei, Lai Dedi, Zhao Lijiao, Liang Yue, Ma Yinglong
• Abstract summary: existing Chains methods are difficult to model and exploit the underlying dependency in the label space. We present a three-phase augmented Chain approach based on co-occurrence analysis for multilabel classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a very popular multi-label classification method, Classifiers Chain has recently been widely applied to many multi-label classification tasks. However, existing Classifier Chains methods are difficult to model and exploit the underlying dependency in the label space, and often suffer from the problems of poorly ordered chain and error propagation. In this paper, we present a three-phase augmented Classifier Chains approach based on co-occurrence analysis for multi-label classification. First, we propose a co-occurrence matrix method to model the underlying correlations between a label and its precedents and further determine the head labels of a chain. Second, we propose two augmented strategies of optimizing the order of labels of a chain to approximate the underlying label correlations in label space, including Greedy Order Classifier Chain and Trigram Order Classifier Chain. Extensive experiments were made over six benchmark datasets, and the experimental results show that the proposed augmented CC approaches can significantly improve the performance of multi-label classification in comparison with CC and its popular variants of Classifier Chains, in particular maintaining lower computational costs while achieving superior performance.

Related papers

• Classifier Chain Networks for Multi-Label Classification [0.0]
  The classifier chain is a widely used method for analyzing multi-labeled data sets. We introduce a generalization of the chain: the classifier chain network.
  arXiv  Detail & Related papers  (2024-11-04T21:56:13Z)
• Label Cluster Chains for Multi-Label Classification [2.072831155509228]
  Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. We propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. Our proposal shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations.
  arXiv  Detail & Related papers  (2024-11-01T11:16:37Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• Multi-label Classification with High-rank and High-order Label Correlations [62.39748565407201]
  Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. We propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification.
  arXiv  Detail & Related papers  (2022-07-09T05:15:31Z)
• Evolving Multi-Label Fuzzy Classifier [5.53329677986653]
  Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-adapt and self-evolve its structure with new incoming multi-label samples in an incremental, single-pass manner.
  arXiv  Detail & Related papers  (2022-03-29T08:01:03Z)
• Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search [90.30623718137244]
  We propose a novel deep hashing method for scalable multi-label image search. A new rank-consistency objective is applied to align the similarity orders from two spaces. A powerful loss function is designed to penalize the samples whose semantic similarity and hamming distance are mismatched.
  arXiv  Detail & Related papers  (2021-02-02T13:46:58Z)
• 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)
• Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation [2.9822184411723645]
  In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. This paper aims at exploiting the high-order label correlation within subsets of labels using a supervised learning classifier system.
  arXiv  Detail & Related papers  (2020-07-22T18:13:12Z)
• Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains [1.357087732949916]
  Dynamic classifier chains (DCC) the label ordering is chosen for each prediction dynamically depending on the respective instance at hand. We combine this concept with the boosting of extreme gradient boosted trees (XGBoost), an effective and scalable state-of-the-art technique. As only positive labels have to be predicted and these are usually only few, the training costs can be substantially reduced.
  arXiv  Detail & Related papers  (2020-06-15T02:35:32Z)
• Interaction Matching for Long-Tail Multi-Label Classification [57.262792333593644]
  We present an elegant and effective approach for addressing limitations in existing multi-label classification models. By performing soft n-gram interaction matching, we match labels with natural language descriptions.
  arXiv  Detail & Related papers  (2020-05-18T15:27:55Z)

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.