Multi-label feature selection based on max-dependency and min-redundancy ... Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection.

Nov 30, 2015 · A metric called max-dependency and min-redundancy is used to evaluate each feature. Extensive experimental results show that the proposed method is effective.

In this paper, we first consider the two factors of multi-label feature, feature dependency and feature redundancy.

Sep 17, 2024 · A metric called max-dependency and min-redundancy is used to evaluate each feature. Extensive experimental results show that the proposed method is effective.

We then propose an evaluation measure that combines mutual information with a max-dependency and min-redundancy algorithm, which allows us to select superior ...

We propose a novel feature selection method for multi-label learning based on the Max-Correlation named Multi-label Feature Selection considering the Max- ...

Oct 22, 2024 · We study how to select good features according to the maximal statistical dependency criterion based on mutual information.

Dec 1, 2021 · Based on information theory, this paper mainly focuses on three key aspects that affect feature relevance: candidate features, selected features ...

A general global optimization framework, in which feature relevance, label relevance, and feature redundancy are taken into account, thus facilitating ...

Mar 13, 2024 · This paper proposes a multi-label static feature selection algorithm to solve the problems caused by high-dimensional features of multi-label learning samples.