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Multi‐objective ensemble generation

WIREs Data Mining and Knowledge Discovery, 2015
Ensemble methods that combine a committee of machine‐learning models, each known as a member or base learner, have gained research interests in the past decade. One interest on ensemble generation involves the multi‐objective approach, which attempts to generate both accurate and diverse members that fulfill the theoretical requirements of good ...
Gu, Shenkai, Cheng, Ran, Jin, Yaochu
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Multi-objective diversity maintenance

Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006
Paul Snijders Kunstmatige Intelligentie Groningen University Grote Kruisstraat 2/1 9712 TS Groningen, The Netherlands P.Snijders@ai.rug.nl Edwin D. de Jong Institute of Information and Computing Sciences Utrecht University PO Box 80.089 3508 TB Utrecht, The Netherlands dejong@cs.uu.nl Bart de Boer Kunstmatige Intelligentie Groningen University Grote ...
Snijders, P.   +3 more
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Multi-Objective Clustering Ensemble

2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006
In this paper we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is constituted by a Pareto-based multi-objective genetic algorithm that uses clustering validation measures as the objective functions.
Faceli, Katti   +2 more
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Multi-Objective Scheduling

2009
no ...
Dutot, Pierre-François   +3 more
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Multi-Objective Interactive Programming

Journal of the Operational Research Society, 1980
This paper proposes a method for finding optimal, or near optimal, solutions for problems involving m objective functions, where there is an overall criterion which is a weighted sum of the m objective functions, but where the weights are, initially, unknown.
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Multi-Objective Decision Making

2017
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book,
Roijers, Diederik, Whiteson, Shimon
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Multi-objective Optimization

2020
In this chapter, we introduce multi-objective optimization, and recall some of the most relevant research articles that appeared in the literature related to this topic. The presented state of the art does not have the purpose of being exhaustive; it aims to drive the reader to the main problems and the approaches to solve them.
Massimiliano Caramia, Paolo Dell’Olmo
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Multi-Objective Recommendations

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
The development of recommender systems usually deal with single-objective optimizations, such as minimizing prediction errors or maximizing the ranking quality. There is an emerging demand in multi-objective recommendations in which the recommendation list can be generated by optimizing multiple objectives.
Yong Zheng, David (Xuejun) Wang
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Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration

2019
Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this
Bezerra, Leonardo C.T.   +2 more
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Multi-objective Decisions

1989
A sign of maturity is the recognition that one can’t have everything. Compromise and trade-off are almost always unavoidable in real life decision situations, not only when several parties with non-coincident interests are involved but also when one own’s objectives or desires compete with one another for attention or priorities.
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