Automated machine learning (AutoML) supports the algorithmic construction and data-
specific customization of machine learning pipelines, including the selection, combination …

The family of methods collectively known as classifier chains has become a popular
approach to multi-label learning problems. This approach involves chaining together off-the …

In multi-label classification, data can have multiple labels simultaneously. Two approaches
to this issue are either transforming the multi-label data or adapting single-label algorithms …

The idea to exploit label dependencies for better prediction is at the core of methods for multi-
label classification (MLC), and performance improvements are normally explained in this …

Abstract Automated Machine Learning (AutoML) has achieved high popularity in recent
years. However, most of these studies have investigated alternatives to single-label …

Robust multi-label classifier chains are difficult to optimise, due to the large search space of
base model types and hyperparameters. This project demonstrates how robust MLC models …

Although automatic ICD coding has achieved some success in English, there still exist
significant challenges for the Chinese electronic medical records (EMRs) based ICD coding …

SHORT DESCRIPTION: In the realm of automated machine learning [1], a frequently
considered problem is to automatically find a suitable composition of machine learning …