Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening

IEEE Trans. Signal Process.

Turğay

2018

In this paper, we propose a new multi-objective contextual multi-armed bandit (MAB) problem with two objectives, where one of the objectives dominates the other objective. Unlike single-objective MAB problems in which the learner obtains a random scalar reward for each arm it selects, in the proposed problem, the learner obtains a random reward vector, where each component of the reward vector corresponds to one of the objectives and the distribution of the reward depends on the context that is provided to the learner at the beginning of each round. We call this problem contextual multi-armed bandit with a dominant objective (CMAB-DO). In CMAB-DO, the goal of the learner is to maximize its total reward in the non-dominant objective while ensuring that it maximizes its total reward in the dominant objective. In this case, the optimal arm given a context is the one that maximizes the expected reward in the non-dominant objective among all arms that maximize the expected reward in the dominant objective. First, we show that the optimal arm lies in the Pareto front. Then, we propose the multi-objective contextual multi-armed bandit algorithm (MOC-MAB), and define two performance measures: the 2-dimensional (2D) regret and the Pareto regret. We show that both the 2D regret and the Pareto regret of MOC-MAB are sublinear in the number of rounds. We also compare the performance of the proposed algorithm with other state-of-the-art methods in synthetic and real-world datasets. The proposed model and the algorithm have a wide range of real-world applications that involve multiple and possibly conflicting objectives ranging from wireless communication to medical diagnosis and recommender systems.

Section: Applications Of Cmab-domentioning

confidence: 99%

Section: B Lexicographic Optimality For D R > 2 Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective Contextual Multi-armed Bandit With a Dominant Objective

IEEE Trans. Signal Process.

Turğay

2018

“…The main goal of diagnosis is to minimize the FPR, given a tolerable threshold for the FNR selected by the system user. In the simulations, the threshold for FNR is set to be 3%, which is considered to be a reasonable level in breast cancer diagnosis [26]. Using this threshold, we can re-characterize the performance metric as follows.…”

Section: A Simulation Setupmentioning

confidence: 99%

Adaptive Ensemble Learning With Confidence Bounds

IEEE Trans. Signal Process.

Yoon

Schaar

2017

Self Cite

Abstract-Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, our approach yields performance guarantees with respect to the optimal local prediction strategy, and is also able to adapt its predictions in a data-driven manner. We illustrate the performance of Hedged Bandits in the context of medical informatics and show that it outperforms numerous online and offline ensemble learning methods.

“…With the rapid increase in the generation speed of the streaming data, online learning methods are becoming increasingly valuable for sequential decision making problems. Many of these problems, ranging from recommender systems [1] to medical screening and diagnosis [2,3] to cognitive radio networks [4] involve multiple and possibly conflicting objectives. In this work, we propose a multi-objective contextual bandit problem with dominant and non-dominant objectives.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective contextual bandits with a dominant objective

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

Turğay

2017

In this paper, we propose a new contextual bandit problem with two objectives, where one of the objectives dominates the other objective. Unlike single-objective bandit problems in which the learner obtains a random scalar reward for each arm it selects, in the proposed problem, the learner obtains a random reward vector, where each component of the reward vector corresponds to one of the objectives. The goal of the learner is to maximize its total reward in the non-dominant objective while ensuring that it maximizes its reward in the dominant objective. In this case, the optimal arm given a context is the one that maximizes the expected reward in the non-dominant objective among all arms that maximize the expected reward in the dominant objective. For this problem, we propose the multi-objective contextual multi-armed bandit algorithm (MOC-MAB), and prove that it achieves sublinear regret with respect to the optimal context dependent policy. Then, we compare the performance of the proposed algorithm with other state-of-the-art bandit algorithms. The proposed contextual bandit model and the algorithm have a wide range of real-world applications that involve multiple and possibly conflicting objectives ranging from wireless communication to medical diagnosis and recommender systems.