Multi-Label Classification

242 papers with code • 9 benchmarks • 24 datasets

Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.

Source: Deep Learning for Multi-label Classification

Libraries

Use these libraries to find Multi-Label Classification models and implementations

Most implemented papers

node2vec: Scalable Feature Learning for Networks

dmlc/dgl 3 Jul 2016

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

Learning to diagnose from scratch by exploiting dependencies among labels

yaoli/chest_xray_14 ICLR 2018

The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

deep-spin/entmax 5 Feb 2016

We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.

Multi-Task Learning as Multi-Objective Optimization

IntelVCL/MultiObjectiveOptimization NeurIPS 2018

These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

baidu/Senta ACL 2020

In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.

SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

dheeraj7596/SCDV EMNLP 2017

We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.

ML-Net: multi-label classification of biomedical texts with deep neural networks

jingcheng-du/ML_Net-1 13 Nov 2018

Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.

Asymmetric Loss For Multi-Label Classification

Alibaba-MIIL/ASL ICCV 2021

In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.

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.

Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

billy-inn/NFETC NAACL 2018

The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text.