Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures

• URL: http://arxiv.org/abs/2011.00109v2
• Date: Mon, 16 Aug 2021 09:38:56 GMT
• Title: Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures
• Authors: Houcemeddine Turki, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha
• Abstract summary: This research paper defines a novel approach that aligns expected labels with predicted labels in multi-label classification. We use it to develop a method for creating precise confusion matrices for a more effective evaluation of multi-label classification algorithms.
• Score: 4.38301148531795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: So far, multi-label classification algorithms have been evaluated using statistical methods that do not consider the semantics of the considered classes and that fully depend on abstract computations such as Bayesian Reasoning. Currently, there are several attempts to develop ontology-based methods for a better assessment of supervised classification algorithms. In this research paper, we define a novel approach that aligns expected labels with predicted labels in multi-label classification using ontology-driven feature-based semantic similarity measures and we use it to develop a method for creating precise confusion matrices for a more effective evaluation of multi-label classification algorithms.

Related papers

• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• Hierarchical confusion matrix for classification performance evaluation [0.0]
  We develop the concept of a hierarchical confusion matrix and prove its applicability to all types of hierarchical classification problems. We use measures based on the novel confusion matrix to evaluate models within a benchmark for three real world hierarchical classification applications. The results outline the reasonability of this approach and its usefulness to evaluate hierarchical classification problems.
  arXiv  Detail & Related papers  (2023-06-15T19:31:59Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• Knowledge Distillation from Single to Multi Labels: an Empirical Study [14.12487391004319]
  We introduce a novel distillation method based on Class Activation Maps (CAMs) Our findings indicate that the logit-based method is not well-suited for multi-label classification. We propose that a suitable dark knowledge should incorporate class-wise information and be highly correlated with the final classification results.
  arXiv  Detail & Related papers  (2023-03-15T04:39:01Z)
• 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)
• Self-Training: A Survey [5.772546394254112]
  Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations. Among the existing techniques, self-training methods have undoubtedly attracted greater attention in recent years. We present self-training methods for binary and multi-class classification; as well as their variants and two related approaches.
  arXiv  Detail & Related papers  (2022-02-24T11:40:44Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Open-Set Representation Learning through Combinatorial Embedding [62.05670732352456]
  We are interested in identifying novel concepts in a dataset through representation learning based on the examples in both labeled and unlabeled classes. We propose a learning approach, which naturally clusters examples in unseen classes using the compositional knowledge given by multiple supervised meta-classifiers on heterogeneous label spaces. The proposed algorithm discovers novel concepts via a joint optimization of enhancing the discrimitiveness of unseen classes as well as learning the representations of known classes generalizable to novel ones.
  arXiv  Detail & Related papers  (2021-06-29T11:51:57Z)
• Multi-label Classification via Adaptive Resonance Theory-based Clustering [9.58897929546191]
  The paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms.
  arXiv  Detail & Related papers  (2021-03-02T06:51:41Z)
• 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)
• 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.