Computing Class Hierarchies from Classifiers

• URL: http://arxiv.org/abs/2112.01187v1
• Date: Thu, 2 Dec 2021 13:01:04 GMT
• Title: Computing Class Hierarchies from Classifiers
• Authors: Kai Kang and Fangzhen Lin
• Abstract summary: We propose a novel algorithm for automatically acquiring a class hierarchy from a neural network. Our algorithm produces surprisingly good hierarchies for some well-known deep neural network models.
• Score: 12.631679928202516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A class or taxonomic hierarchy is often manually constructed, and part of our knowledge about the world. In this paper, we propose a novel algorithm for automatically acquiring a class hierarchy from a classifier which is often a large neural network these days. The information that we need from a classifier is its confusion matrix which contains, for each pair of base classes, the number of errors the classifier makes by mistaking one for another. Our algorithm produces surprisingly good hierarchies for some well-known deep neural network models trained on the CIFAR-10 dataset, a neural network model for predicting the native language of a non-native English speaker, a neural network model for detecting the language of a written text, and a classifier for identifying music genre. In the literature, such class hierarchies have been used to provide interpretability to the neural networks. We also discuss some other potential uses of the acquired hierarchies.

Related papers

• Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
  Formal language theory pertains specifically to recognizers. It is common to instead use proxy tasks that are similar in only an informal sense. We correct this mismatch by training and evaluating neural networks directly as binary classifiers of strings.
  arXiv  Detail & Related papers  (2024-11-11T16:33:25Z)
• How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model [47.617093812158366]
  We introduce the Random Hierarchy Model: a family of synthetic tasks inspired by the hierarchical structure of language and images. We find that deep networks learn the task by developing internal representations invariant to exchanging equivalent groups. Our results indicate how deep networks overcome the curse of dimensionality by building invariant representations.
  arXiv  Detail & Related papers  (2023-07-05T09:11:09Z)
• Neuro-symbolic Rule Learning in Real-world Classification Tasks [75.0907310059298]
  We extend pix2rule's neural DNF module to support rule learning in real-world multi-class and multi-label classification tasks. We propose a novel extended model called neural DNF-EO (Exactly One) which enforces mutual exclusivity in multi-class classification.
  arXiv  Detail & Related papers  (2023-03-29T13:27:14Z)
• A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification [29.075766631810595]
  We propose a Symbolic-Neural model that can learn to explicitly predict class labels of text spans from a constituency tree. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data. Our experiments demonstrate that our approach could achieve good prediction accuracy in downstream tasks.
  arXiv  Detail & Related papers  (2023-03-06T03:25:43Z)
• Do We Really Need a Learnable Classifier at the End of Deep Neural Network? [118.18554882199676]
  We study the potential of learning a neural network for classification with the classifier randomly as an ETF and fixed during training. Our experimental results show that our method is able to achieve similar performances on image classification for balanced datasets.
  arXiv  Detail & Related papers  (2022-03-17T04:34:28Z)
• Khmer Text Classification Using Word Embedding and Neural Networks [0.0]
  We discuss various classification approaches for Khmer text. A Khmer word embedding model is trained on a 30-million-Khmer-word corpus to construct word vector representations. We evaluate the performance of different approaches on a news article dataset for both multi-class and multi-label text classification tasks.
  arXiv  Detail & Related papers  (2021-12-13T15:57:32Z)
• Incremental Deep Neural Network Learning using Classification Confidence Thresholding [4.061135251278187]
  Most modern neural networks for classification fail to take into account the concept of the unknown. This paper proposes the Classification Confidence Threshold approach to prime neural networks for incremental learning.
  arXiv  Detail & Related papers  (2021-06-21T22:46:28Z)
• Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers [54.47911829539919]
  We develop a novel top-down training method which can be viewed as an algorithm for searching for high-quality classifiers. We tested this method on automatic speech recognition (ASR) tasks and language modelling tasks. The proposed method consistently improves recurrent neural network ASR models on Wall Street Journal, self-attention ASR models on Switchboard, and AWD-LSTM language models on WikiText-2.
  arXiv  Detail & Related papers  (2021-02-09T08:19:49Z)
• A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism [2.3204178451683264]
  We introduce a new attention-based neural network architecture called Audio-Based Convolutional Neural Network (CAB-CNN) The algorithm uses a newly designed architecture consisting of a list of simple classifiers and an attention mechanism as a selector. Compared to the state-of-the-art algorithms, our algorithm achieves more than 10% improvements on all selected test scores.
  arXiv  Detail & Related papers  (2020-06-14T21:29:44Z)
• Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers [48.11796810425477]
  We show that IB learning is, in fact, equivalent to a special class of the quantization problem. We propose a novel learning framework, "Aggregated Learning", for classification with neural network models. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.
  arXiv  Detail & Related papers  (2020-01-12T16:22:24Z)

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.