Extreme Multi-Label Classification

30 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.

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3 papers
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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

xmc-aalto/bonsai 17 Apr 2019

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

Refefer/fastxml 8 Sep 2016

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 7 May 2019

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

mwydmuch/napkinXC 23 Sep 2020

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

kareldo/xmc.dspy 22 Jan 2024

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 12 Apr 2017

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

hiro4bbh/sticker 12 Feb 2018

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

xmc-aalto/proxml 5 Mar 2018

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.