A literature survey on algorithms for multi-label learning

Abstract

Multi-label Learning is a form of supervised learning where the classification al- gorithm is required to learn from a set of instances, each instance can belong to multiple classes and so after be able to predict a set of class labels for a new in- stance. This is a generalized version of most popular multi-class problems where each instances is restricted to have only one class label. There exists a wide range of applications for multi-labelled predictions, such as text categorization, seman- tic image labeling, gene functionality classification etc. and the scope and interest is increasing with modern applications. This survey paper introduces the task of multi-label prediction (classification), presents the sparse literature in this area in an organized manner, discusses different evaluation metrics and performs a com- parative analysis of the existing algorithms. This paper also relates multi-label problems with similar but different problems that are often reduced to multi-label problems to have access to wide range of multi-label algorithms.