Multi-Label Classification Using Link Prediction

TL;DR

This paper extends the CULP graph-based link prediction method to multi-label classification, demonstrating competitive results and maintaining high accuracy and efficiency in predicting multiple labels per instance.

Contribution

The paper introduces a novel extension of the CULP algorithm for multi-label classification, leveraging graph representations to handle multiple labels per data point.

Findings

Achieves competitive accuracy with existing multi-label classifiers.

Maintains near constant time prediction for multi-label data.

Effectively models multi-label data using graph-based link prediction.

Abstract

Solving classification with graph methods has gained huge popularity in recent years. This is due to the fact that the data can be intuitively modeled with graphs to utilize high level features to aid in solving the classification problem. CULP which is short for Classification Using Link Prediction is a graph-based classifier. This classifier utilizes the graph representation of the data and transforms the problem to that of link prediction where we try to find the link between an unlabeled node and the proper class node for it. CULP proved to be highly accurate classifier and it has the power to predict the labels in near constant time. A variant of the classification problem is multi-label classification which tackles this problem for multi-label data where an instance can have multiple labels associated to it. In this work, we extend the CULP algorithm to address this problem. Our proposed extensions conveys the powers of CULP and its intuitive representation of the data in to the multi-label domain and in comparison to some of the cutting edge multi-label classifiers, yield competitive results.