Adaptive Ensemble Learning With Confidence Bounds

Tekin, Cem; Yoon, Jinsung; Schaar, Mihaela van der

doi:10.1109/tsp.2016.2626250

Cited by 33 publications

(37 citation statements)

References 38 publications

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“…Though the algorithm parameter h T and the regret upper bound is given based on a known time horizon T , COERR can be easily extended to work with unknown time horizon with the assistance of doubling-trick [36], [37]. The key idea of doubling-trick is to partition the time into multiple phases (j = 1, 2, 3, ...) with doubling length (T 1 , T 2 , · · · ), e.g., if the length of phase is T 1 = T , then the length of j-th phase is 2 j−1 T .…”

Section: Example: Maximum Likelihood Estimatormentioning

confidence: 99%

Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Chen

2019

IEEE J. Select. Areas Commun.

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Shared edge computing platforms, which enable Application Service Providers (ASPs) to deploy applications in close proximity to mobile users are providing ultra-low latency and location-awareness to a rich portfolio of services. Though ubiquitous edge service provisioning, i.e., deploying the application at all possible edge sites, is always preferable, it is impractical due to often limited operational budget of ASPs. In this case, an ASP has to cautiously decide where to deploy the edge service and how much budget it is willing to use. A central issue here is that the service demand received by each edge site, which is the key factor of deploying benefit, is unknown to ASPs a priori. What's more complicated is that this demand pattern varies temporally and spatially across geographically distributed edge sites. In this paper, we investigate an edge resource rental problem where the ASP learns service demand patterns for individual edge sites while renting computation resource at these sites to host

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Section: Example: Maximum Likelihood Estimatormentioning

confidence: 99%

Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Chen

2019

IEEE J. Select. Areas Commun.

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“…The real-time classification algorithms have emerged that analyze the decision making process and make improvements in real-time. To provide an expert system, one needs to answer the following questions online [14]: What will be the cost for wrong decision? What will be the weights for different classifiers?…”

Section: Introductionmentioning

confidence: 99%

Expert advice ensemble for thyroid disease diagnosis

Qureshi

Eksioglu

2017

2017 25th Signal Processing and Communications Applications Conference (SIU)

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Thyroid gland influences the metabolic processes of human body due to the fact that it produces hormones. Hyperthyroidism in caused due to increase in the production of thyroid hormones. In this paper a methodology using an online ensemble of decision trees to detect thyroid-related diseases is proposed. The aim of this work is to improve the diagnostic accuracy of thyroid disease. Initially, feature rejection method is applied to discard 10 irrelevant and redundant features from 29 features. Then, it's shown that the offline ensemble of decision trees provides higher performance than state-of-the-art methodologies. Afterwards, the exponential weights based online ensemble method is implemented which reaches comparable classification performance with offline methodology. The proposed system consists of three stages: feature rejection, training decision trees with different cost schemes and the online classification stage where each classifier is weighted using an exponential weight based algorithm. The performance of online algorithm increases as the number of samples increases, because it continuously updates the weights to improve accuracy. The achieved classification accuracy proves the robustness and effectiveness of online version of proposed system in thyroid disease diagnosis.

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“…These streams are then mined in realtime to assist numerous semantic computing applications (see Fig. 1): patient monitoring, 1 personalized diagnosis, 2,3 personalized treatment recommendation, 4,5 recommender systems, [6][7][8] social networks, 9 network security, 10 multimedia content aggregation, 11 personalized education 12,13 etc. Hence, online data mining systems have emerged that enable such applications to analyze, extract actionable intelligence and make decisions in real-time, based on the correlated, highdimensional and dynamic data captured by multiple heterogeneous data sources.…”

Section: Introductionmentioning

confidence: 99%

Actionable intelligence and online learning for semantic computing

Tekin

Schaar

2017

Encyclopedia Semant. Comput. Robot. Int.

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As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data streams and make timely decisions based on this knowledge. This requires that appropriate classifiers are invoked to process the incoming streams and find the relevant knowledge. Thus, a key challenge becomes choosing online, at run-time, which classifier should be deployed to make the best possible predictions on the incoming streams. In this paper, we survey a class of methods capable to perform online learning in stream-based semantic computing tasks: multi-armed bandits (MABs). Adopting MABs for stream mining poses, numerous new challenges requires many new innovations. Most importantly, the MABs will need to explicitly consider and track online the time-varying characteristics of the data streams and to learn fast what is the relevant information out of the vast, heterogeneous and possibly highly dimensional data streams. In this paper, we discuss contextual MAB methods, which use similarities in context (meta-data) information to make decisions, and discuss their advantages when applied to stream mining for semantic computing. These methods can be adapted to discover in real-time the relevant contexts guiding the stream mining decisions, and tract the best classifier in presence of concept drift. Moreover, we also discuss how stream mining of multiple data sources can be performed by deploying cooperative MAB solutions and ensemble learning. We conclude the paper by discussing the numerous other advantages of MABs that will benefit semantic computing applications.

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Adaptive Ensemble Learning With Confidence Bounds

Cited by 33 publications

References 38 publications

Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Expert advice ensemble for thyroid disease diagnosis

Actionable intelligence and online learning for semantic computing

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