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
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Libraries
Use these libraries to find Extreme Multi-Label Classification models and implementations
LSHTC
EXTREME CLASSIFICATION
Most implemented papers
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
amzn/pecos
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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
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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
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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
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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.
