A Survey on Multi-output Learning

01/02/2019
by   Donna Xu, et al.
38

Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making in real-world applications. Inspired by big data, the 4Vs characteristics of multi-output imposes a set of challenges to multi-output learning, in terms of the volume, velocity, variety and veracity of the outputs. Increasing number of works in the literature have been devoted to the study of multi-output learning and the development of novel approaches for addressing the challenges encountered. However, it lacks a comprehensive overview on different types of challenges of multi-output learning brought by the characteristics of the multiple outputs and the techniques proposed to overcome the challenges. This paper thus attempts to fill in this gap to provide a comprehensive review on this area. We first introduce different stages of the life cycle of the output labels. Then we present the paradigm on multi-output learning, including its myriads of output structures, definitions of its different sub-problems, model evaluation metrics and popular data repositories used in the study. Subsequently, we review a number of state-of-the-art multi-output learning methods, which are categorized based on the challenges.

READ FULL TEXT

page 1

page 3

page 5

page 11

research
03/17/2021

Set-to-Sequence Methods in Machine Learning: a Review

Machine learning on sets towards sequential output is an important and u...
research
07/16/2020

Learning from Noisy Labels with Deep Neural Networks: A Survey

Deep learning has achieved remarkable success in numerous domains with h...
research
07/05/2021

Deep Learning Schema-based Event Extraction: Literature Review and Current Trends

Schema-based event extraction is a critical technique to apprehend the e...
research
09/14/2021

Network representation learning systematic review: ancestors and current development state

Real-world information networks are increasingly occurring across variou...
research
07/19/2020

Event Prediction in the Big Data Era: A Systematic Survey

Events are occurrences in specific locations, time, and semantics that n...
research
06/24/2019

AMIC: An Adaptive Information Theoretic Method to Identify Multi-Scale Temporal Correlations in Big Time Series Data

Recent development in computing, sensing and crowd-sourced data have res...
research
12/21/2021

Design And Analysis Of Three-Output Open Differential with 3-DOF

This paper presents a novel passive three-output differential with three...

Please sign up or login with your details

Forgot password? Click here to reset