Extreme Multi-Label Classification
29 papers with code • 0 benchmarks • 2 datasets
Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.
Benchmarks
These leaderboards are used to track progress in Extreme Multi-Label Classification
Libraries
Use these libraries to find Extreme Multi-Label Classification models and implementations
EXTREME CLASSIFICATION
LSHTC
Most implemented papers
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
amzn/pecos
•
•
ICLR 2022
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
Taming Pretrained Transformers for Extreme Multi-label Text Classification
OctoberChang/X-Transformer
•
•
However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.
Probabilistic Label Trees for Extreme Multi-label Classification
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
Generalized test utilities for long-tail performance in extreme multi-label classification
mwydmuch/xcolumns
•
•
NeurIPS 2023
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
Deep Extreme Multi-label Learning
theGuyWithBlackTie/Deep-Extreme-Multi-Label-Learning
•
•
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data.
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
Finally, we show that the Sparse Weighted Nearest-Neighbor Method can process data points in real time on XMLC datasets with equivalent performance to SOTA models, with a single thread and smaller storage footprint.
Adversarial Extreme Multi-label Classification
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels.
