Work

GKF-PUAL: A group kernel-free approach to positive-unlabeled learning with variable selection

Year: 2024

Type: article

Abstract: Variable selection is important for classification of data with many irrelevant predicting variables, but it has not yet been well studied in positive-unlabeled (PU) learning, where classifiers have to be trained without labelled-negative instances. In this paper, we propose a group kernel-free PU classifier with asymmetric loss (GKF-PUAL) to achieve quadratic PU classification with group-lasso regularisation embedded for variable selection. We also propose a five-block algorithm to solve the optimization problem of GKF-PUAL. Our experimental results reveal the superiority of GKF-PUAL in both PU classification and variable selection, improving the baseline PUAL by more than 10% in F1-score across four benchmark datasets and removing over 70% of irrelevant variables on six benchmark datasets. The code for GKF-PUAL is at https://github.com/tkks22123/GKF-PUAL . • We propose a group kernel-free PU classifier (GKF-PUAL) with variable selection. • We propose a five-block algorithm for optimization of GKF-PUAL. • Experimental results verify the superiority of GKF-PUAL.

Source: Information Sciences

DOI: 10.1016/j.ins.2024.121574

Authors: Xiaoke Wang, Rui Zhu, Jing-Hao Xue

Institutions: University College London, City, University of London, City St George's, University of London

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,360

OpenAlex ID: W4403635621

FWCI: 0.147

Cites: 35

Cited by: 1

Related to: 10