Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks

• URL: http://arxiv.org/abs/2409.08647v2
• Date: Mon, 06 Jan 2025 09:05:14 GMT
• Title: Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks
• Authors: Anita Eisenbürger, Daniel Otten, Anselm Hudde, Frank Hopfgartner,
• Abstract summary: This study explores the impact of label noise on gradient-boosted decision trees (GBDTs) We adapt two noise detection methods from deep learning for use with GBDTs and introduce a new detection method called Gradients. Our noise detection methods achieve state-of-the-art results, with a noise detection accuracy above 99% on the Adult dataset across all noise levels.
• Score: 1.261491746208123
• License:
• Abstract: Label noise, which refers to the mislabeling of instances in a dataset, can significantly impair classifier performance, increase model complexity, and affect feature selection. While most research has concentrated on deep neural networks for image and text data, this study explores the impact of label noise on gradient-boosted decision trees (GBDTs), the leading algorithm for tabular data. This research fills a gap by examining the robustness of GBDTs to label noise, focusing on adapting two noise detection methods from deep learning for use with GBDTs and introducing a new detection method called Gradients. Additionally, we extend a method initially designed for GBDTs to incorporate relabeling. By using diverse datasets such as Covertype and Breast Cancer, we systematically introduce varying levels of label noise and evaluate the effectiveness of early stopping and noise detection methods in maintaining model performance. Our noise detection methods achieve state-of-the-art results, with a noise detection accuracy above 99% on the Adult dataset across all noise levels. This work enhances the understanding of label noise in GBDTs and provides a foundation for future research in noise detection and correction methods.

Related papers

• Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection [84.78475642696137]
  The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. We propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS) SGPS constructs reliable positive pairs for noisy samples to enhance the sample utilization.
  arXiv  Detail & Related papers  (2025-01-19T14:41:55Z)
• NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification [7.464154519547575]
  Existing research on learning with noisy labels predominantly focuses on synthetic noise patterns. We constructed a benchmark dataset to better understand label noise in real-world text classification settings. Our findings reveal that while pre-trained models are resilient to synthetic noise, they struggle against instance-dependent noise.
  arXiv  Detail & Related papers  (2024-07-09T06:18:40Z)
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features [43.41573458276422]
  We introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network. The proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation.
  arXiv  Detail & Related papers  (2022-12-19T09:39:30Z)
• Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning [22.02829139522153]
  We propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data.
  arXiv  Detail & Related papers  (2022-12-01T13:05:20Z)
• Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile [78.1212767880785]
  meta-learner is prone to overfitting since there are only a few available samples. When handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise. We present Eigen-Reptile (ER) that updates the meta- parameters with the main direction of historical task-specific parameters.
  arXiv  Detail & Related papers  (2022-06-04T08:48:02Z)
• Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model [26.76830553508229]
  Hypergeometric Learning (HGL) is a denoising algorithm for distantly supervised named entity recognition. HGL takes both noise distribution and instance-level confidence into consideration. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision.
  arXiv  Detail & Related papers  (2021-06-17T04:01:25Z)
• Training Classifiers that are Universally Robust to All Label Noise Levels [91.13870793906968]
  Deep neural networks are prone to overfitting in the presence of label noise. We propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. Our framework generally outperforms at medium to high noise levels.
  arXiv  Detail & Related papers  (2021-05-27T13:49:31Z)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• Towards Robustness to Label Noise in Text Classification via Noise Modeling [7.863638253070439]
  Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to capture this noise through an auxiliary noise model over the classifier.
  arXiv  Detail & Related papers  (2021-01-27T05:41:57Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.