Active Bipartite Ranking - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Active Bipartite Ranking

Abstract

In this paper, we develop an active learning framework for the bipartite ranking problem. Motivated by numerous applications, ranging from supervised anomaly detection to credit-scoring through the design of medical diagnosis support systems, and usually formulated as the problem of optimizing (a scalar summary of) the ROC curve, bipartite ranking has been the subject of much attention in the passive context. Various dedicated algorithms have been recently proposed and studied by the machine-learning community. In contrast, active bipartite ranking rule is poorly documented in the literature. Due to its global nature, a strategy for labeling sequentially data points that are difficult to rank w.r.t. to the others is required. This learning task is much more complex than binary classification, for which many active algorithms have been designed. It is the goal of this article to provide a rigorous formulation of such a selective sampling approach. We propose a dedicated algorithm, referred to as active-rank, which aims to minimise the distance between the ROC curve of the ranking function built and the optimal one, w.r.t. the sup norm. We show that, for a fixed confidence level ε and probability δ, active-rank is PAC(ε, δ). In addition, we provide a problem dependent upper bound on the expected sampling time of active-rank and also demonstrate a problem dependent lower bound on the expected sampling time of any PAC(ε, δ) algorithm. Beyond the theoretical analysis carried out, numerical results are presented, providing strong empirical evidence of the performance of the algorithm proposed, which compares favorably with more naive approaches.
Fichier principal
Vignette du fichier
active_ranking-25.pdf (925.07 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Licence : Public Domain

Dates and versions

hal-04242023 , version 1 (14-10-2023)
hal-04242023 , version 2 (24-01-2024)

Identifiers

  • HAL Id : hal-04242023 , version 2

Cite

James Cheshire, Stéphan Clémençon, Vincent Laurent. Active Bipartite Ranking. Thirty-seventh Annual Conference on Neural Information Processing Systems, Dec 2023, New Orleans, United States. ⟨hal-04242023v2⟩
165 View
56 Download

Share

Gmail Facebook X LinkedIn More