RLSEP: Learning Label Ranks for Multi-label Classification

2022-12-08 · Emine Dari, V. Bugra Yesilkaynak, Alican Mertan, Gozde Unal

Multi-label ranking maps instances to a ranked set of predicted labels from multiple possible classes. The ranking approach for multi-label learning problems received attention for its success in multi-label classification, with one of the well-known approaches being pairwise label ranking. However, most existing methods assume that only partial information about the preference relation is known, which is inferred from the partition of labels into a positive and negative set, then treat labels with equal importance. In this paper, we focus on the unique challenge of ranking when the order of the true label set is provided. We propose a novel dedicated loss function to optimize models by incorporating penalties for incorrectly ranked pairs, and make use of the ranking information present in the input. Our method achieves the best reported performance measures on both synthetic and real world ranked datasets and shows improvements on overall ranking of labels. Our experimental results demonstrate that our approach is generalizable to a variety of multi-label classification and ranking tasks, while revealing a calibration towards a certain ranking ordering.

📄 PDF Abstract BibTeX arXiv:2212.04022

Code (1)

MrGranddy/GaussianMLR 공식 구현 pytorch

Tasks

Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION Multi-Label Learning

RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

2022-05-30 · Yu Gong, Greg Mori, Frederick Tung

Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks. Unlike classification, in regression the labels are continuous, potenti…

Deep imbalanced regression Inductive Bias regression STS+1

RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation

2022-03-08 · Haodi He, Yuhui Yuan, Xiangyu Yue, Han Hu

The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic categories shared by all images or videos…

Classification Instance Segmentation Multi-Label Classification MUlTI-LABEL-ClASSIFICATION+6

Identifying Incorrect Annotations in Multi-Label Classification Data

2022-11-25 · Aditya Thyagarajan, Elías Snorrason, Curtis Northcutt, Jonas Mueller

In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes). Example applications include image (or document) tagging where each possible tag eit…

Classification Label Error Detection Multi-Label Classification MUlTI-LABEL-ClASSIFICATION+1

ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification

2023-11-16 · Yaxin Zhu, Hamed Zamani

This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on …

Extreme Multi-Label Classification In-Context Learning Multi-Label Classification MUlTI-LABEL-ClASSIFICATION

Nearest Labelset Using Double Distances for Multi-label Classification

2017-02-15 · Hyukjun Gweon, Matthias Schonlau, Stefan Steiner

Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation betwe…

Classification General Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION