MLCBART: Multilabel Classification with Bayesian Additive Regression Trees

• URL: http://arxiv.org/abs/2601.08964v1
• Date: Tue, 13 Jan 2026 20:17:45 GMT
• Title: MLCBART: Multilabel Classification with Bayesian Additive Regression Trees
• Authors: Jiahao Tian, Hugh Chipman, Thomas Loughin,
• Abstract summary: Multilabel Classification deals with the simultaneous classification of multiple binary labels.<n>BART is a nonparametric and flexible model structure capable of uncovering complex relationships within the data.<n>Our adaptation, MLCBART, assumes that labels arise from thresholding an underlying numeric scale.
• Score: 0.6117371161379209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilabel Classification (MLC) deals with the simultaneous classification of multiple binary labels. The task is challenging because, not only may there be arbitrarily different and complex relationships between predictor variables and each label, but associations among labels may exist even after accounting for effects of predictor variables. In this paper, we present a Bayesian additive regression tree (BART) framework to model the problem. BART is a nonparametric and flexible model structure capable of uncovering complex relationships within the data. Our adaptation, MLCBART, assumes that labels arise from thresholding an underlying numeric scale, where a multivariate normal model allows explicit estimation of the correlation structure among labels. This enables the discovery of complicated relationships in various forms and improves MLC predictive performance. Our Bayesian framework not only enables uncertainty quantification for each predicted label, but our MCMC draws produce an estimated conditional probability distribution of label combinations for any predictor values. Simulation experiments demonstrate the effectiveness of the proposed model by comparing its performance with a set of models, including the oracle model with the correct functional form. Results show that our model predicts vectors of labels more accurately than other contenders and its performance is close to the oracle model. An example highlights how the method's ability to produce measures of uncertainty on predictions provides nuanced understanding of classification results.

Related papers

• SSLfmm: An R Package for Semi-Supervised Learning with a Mixed-Missingness Mechanism in Finite Mixture Models [2.0253523660913664]
  Semi-supervised learning (SSL) constructs classifiers from datasets in which only a subset of observations is labelled.<n>The missingness process can be informative, as the chances of an observation being unlabelled may depend on the ambiguity of its feature vector.<n>This package includes a practical tool for modelling and illustrates its performance through simulated examples.
  arXiv  Detail & Related papers  (2025-12-03T00:14:33Z)
• Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification [0.5624791703748108]
  We investigate multi-label classification involving large sets of labels, where the output labels may be known to satisfy some logical constraints.<n>We look at an architecture in which classifiers for individual labels are fed into an expressive sequential model, which produces a joint distribution.
  arXiv  Detail & Related papers  (2025-07-20T23:31:36Z)
• Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection [29.30585064760435]
  We propose an Adaptive Collaborative Correlation lEarning-based Semi-Supervised Multi-label Feature Selection (Access-MFS) method to address these issues.<n> Specifically, a generalized regression model equipped with an extended uncorrelated constraint is introduced to select discriminative yet irrelevant features.<n>The correlation instance and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph.
  arXiv  Detail & Related papers  (2024-06-18T01:47:38Z)
• Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification [49.09505771145326]
  We propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-04-26T06:00:27Z)
• Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks [75.42002070547267]
  We propose a self evolution learning (SE) based mixup approach for data augmentation in text classification. We introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up.
  arXiv  Detail & Related papers  (2023-05-22T23:43:23Z)
• Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision [75.1860418333995]
  Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources as labeling functions. Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process.
  arXiv  Detail & Related papers  (2022-10-06T07:28:53Z)
• Active Learning by Feature Mixing [52.16150629234465]
  We propose a novel method for batch active learning called ALFA-Mix. We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions. We show that inconsistencies in these predictions help discovering features that the model is unable to recognise in the unlabelled instances.
  arXiv  Detail & Related papers  (2022-03-14T12:20:54Z)
• Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification [27.043136219527767]
  We propose a novel contrastive learning boosted multi-label prediction model. By using contrastive learning in the supervised setting, we can exploit label information effectively. We show that the learnt embeddings provide insights into the interpretation of label-label interactions.
  arXiv  Detail & Related papers  (2021-12-02T04:23:34Z)
• CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator [60.799183326613395]
  We propose an unbiased estimator for categorical random variables based on multiple mutually negatively correlated (jointly antithetic) samples. CARMS combines REINFORCE with copula based sampling to avoid duplicate samples and reduce its variance, while keeping the estimator unbiased using importance sampling. We evaluate CARMS on several benchmark datasets on a generative modeling task, as well as a structured output prediction task, and find it to outperform competing methods including a strong self-control baseline.
  arXiv  Detail & Related papers  (2021-10-26T20:14:30Z)
• Learning from Aggregate Observations [82.44304647051243]
  We study the problem of learning from aggregate observations where supervision signals are given to sets of instances. We present a general probabilistic framework that accommodates a variety of aggregate observations. Simple maximum likelihood solutions can be applied to various differentiable models.
  arXiv  Detail & Related papers  (2020-04-14T06:18:50Z)

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.