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A sensor fusion framework for online sensor and algorithm selection

ROBOTICS AND AUTONOMOUS SYSTEMS 2008年第9期56卷 762-776页

作者： Cohen, Ofir Edan, Yael Ben Gurion Univ Negev Dept Ind Engn & Management IL-84105 Beer Sheva Israel Ben Gurian Int Airport Israel Aircraft Ind Ltd Mil Aircraft Grp Lahav Div IL-70100 Beer Sheva Israel

This paper presents a sensor fusion framework framework for selecting online the most reliable logical sensors and the most suitable algorithm for fusing sensor data in a robot platform. The framework is rule-based, employing the concept of using the simplest sensor fusion algorithm with the most reliable sensors. The framework is realized by implementing measures that were developed to quantify online sensor performance. Statistical, histogram, time series and graphical analyses demonstrate the advantages of this new framework. (C) Elsevier B.V. All rights reserved.

关键词： algorithm selection mobile robots performance measures sensor fusion

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A PAC APPROACH TO APPLICATION-SPECIFIC algorithm selection

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SIAM JOURNAL ON COMPUTING 2017年第3期46卷 992-1017页

作者： Gupta, Rishi Roughgarden, Tim Stanford Univ Dept Comp Sci Stanford CA 94305 USA

The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. While there is a large body of literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. This paper adapts concepts from statistical and online learning theory to reason about application-specific algorithm selection. Our models capture several state-of-the-art empirical and theoretical approaches to the problem, ranging from self-improving algorithms to empirical performance models, and our results identify conditions under which these approaches are guaranteed to perform well. We present one framework that models algorithm selection as a statistical learning problem, and our work here shows that dimension notions from statistical learning theory, historically used to measure the complexity of classes of binary-and real-valued functions, are relevant in a much broader algorithmic context. We also study the online version of the algorithm selection problem, and give possibility and impossibility results for the existence of no-regret learning algorithms.

关键词： algorithm selection parameter tuning PAC learning online learning meta-algorithms

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Reducing Cognitive Overload by Meta-Learning Assisted algorithm selection

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2008年第3期2卷 90-100页

作者： Fan, Lisa Lei, Minxiao Univ Regina Regina SK Canada

With the explosion of aver/able data mining algorithms, a method 'Or helping user to select the most appropriate algorithm or combination of algorithms to solve a given problem and reducing users' cognitive overload due to the overloaded data mining algorithms is becoming increasingly important. In this article, we have presented a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The article discusses the meta-learning method in details and presents some empirical results that show the improvement we can achieve with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the damsel can be found. Thus, we can speed up the ranking process and increase the accuracy by using the reduct of the properties of dataset. Uitli the reduced searching space, users' cognitive load is reduced.

关键词： algorithm selection cognitive overload meta-learning rough set

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When algorithm selection meets Bi-linear Learning to Rank: accuracy and inference time trade off with candidates expansion

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2023年第2期16卷 173-189页

作者： Yuan, Jing Geissler, Christian Shao, Weijia Lommatzsch, Andreas Jain, Brijnesh Tech Univ Berlin TEL-14Ernst Reuter Pl 7 D-10587 Berlin Germany

algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances' meta-features and algorithms' landmark performances, Machine Learning (ML) approaches are applied to solve AS problems. However, the standard training process of benchmark ML approaches in AS either needs to train the models specifically for every algorithm or relies on the sparse one-hot encoding as the algorithms' representation. To escape these intermediate steps and form the mapping function directly, we borrow the learning to rank framework from Recommender System (RS) and embed the bi-linear factorization to model the algorithms' performances in AS. This Bi-linear Learning to Rank (BLR) has proven to work with competence in some AS scenarios and thus is also proposed as a benchmark approach. Thinking from the evaluation perspective in the modern AS challenges, precisely predicting the performance is usually the measuring goal. Though approaches' inference time also needs to be counted for the running time cost calculation, it's always overlooked in the evaluation process. The multi-objective evaluation metric Adjusted Ratio of Root Ratios (A3R) is therefore advocated in this paper to balance the trade-off between the accuracy and inference time in AS. Concerning A3R, BLR outperforms other benchmarks when expanding the candidates range toTOP3. The better effect of this candidates expansion results from the cumulative optimum performance during the AS process. We take the further step in the experimentation to represent the advantage of suchTOPKexpansion, and illustrate that such expansion can be considered as the supplement for the convention ofTOP1 selection during the evaluation process.

关键词： algorithm selection Bi-linear Learning to Rank Multi-object evaluation Candidates expansion

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MetaStream: A meta-learning based method for periodic algorithm selection in time-changing data

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NEUROCOMPUTING 2014年第1期127卷 52-64页

作者： Debiaso Rossi, Andre Luis de Leon Ferreira de Carvalho, Andre Carlos Ponce Soares, Carlos de Souza, Bruno Feres Univ Sao Paulo Inst Ciencias Matemat & Computac Sao Carlos SP Brazil Univ Porto Fac Engn INESC TEC P-4100 Oporto Portugal

Dynamic real-world applications that generate data continuously have introduced new challenges for the machine learning community, since the concepts to be learned are likely to change over time. In such scenarios, an appropriate model at a time point may rapidly become obsolete, requiring updating or replacement. As there are several learning algorithms available, choosing one whose bias suits the current data best is not a trivial task. In this paper, we present a meta-learning based method for periodic algorithm selection in time-changing environments, named MetaStream. It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination. Experimental results for two real regression problems showed that MetaStream is able to improve the general performance of the learning system compared to a baseline method and an ensemble-based approach. (C) 2013 Elsevier B.V. All rights reserved.

关键词： algorithm selection Meta-learning Data streams

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Automated algorithm selection: from Feature-Based to Feature-Free Approaches

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JOURNAL OF HEURISTICS 2023年第1期29卷 1-38页

作者： Alissa, Mohamad Sim, Kevin Hart, Emma Edinburgh Napier Univ Sch Comp Edinburgh Scotland

We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural networks to predict a packing heuristic in online bin-packing, selecting from four well-known heuristics. As input, the RNN methods only use the sequence of item-sizes. This contrasts to typical approaches to algorithm-selection which require a model to be trained using domain-specific instance features that need to be first derived from the input data. The RNN approaches are shown to be capable of achieving within 5% of the oracle performance on between 80.88 and 97.63% of the instances, depending on the dataset. They are also shown to outperform classical machine learning models trained using derived features. Finally, we hypothesise that the proposed methods perform well when the instances exhibit some implicit structure that results in discriminatory performance with respect to a set of heuristics. We test this hypothesis by generating fourteen new datasets with increasing levels of structure, and show that there is a critical threshold of structure required before algorithm-selection delivers benefit.

关键词： Deep learning Machine learning Recurrent neural network algorithm selection Bin-packing problem Feature-free approach

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Deep Reinforcement Learning for Dynamic algorithm selection: A Proof-of-Principle Study on Differential Evolution

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第7期54卷 4247-4259页

作者： Guo, Hongshu Ma, Yining Ma, Zeyuan Chen, Jiacheng Zhang, Xinglin Cao, Zhiguang Zhang, Jun Gong, Yue-Jiao South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Natl Univ Singapore Coll Design & Engn Dept Ind Syst Engn & Management Singapore Singapore Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore Nankai Univ Tianjin 300192 Peoples R China Hanyang Univ Seoul 04763 South Korea Univ Victoria Melbourne Vic 3004 Australia

Evolutionary algorithms, such as differential evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algorithm selection or configuration. This article aims to address the limitation by leveraging the complementary strengths of a group of algorithms and dynamically scheduling them throughout the optimization progress for specific problems. We propose a deep reinforcement learning-based dynamic algorithm selection framework to accomplish this task. Our approach models the dynamic algorithm selection a Markov decision process, training an agent in a policy gradient manner to select the most suitable algorithm according to the features observed during the optimization process. To empower the agent with the necessary information, our framework incorporates a thoughtful design of landscape and algorithmic features. Meanwhile, we employ a sophisticated deep neural network model to infer the optimal action, ensuring informed algorithm selections. Additionally, an algorithm context restoration mechanism is embedded to facilitate smooth switching among different algorithms. These mechanisms together enable our framework to seamlessly select and switch algorithms in a dynamic online fashion. Notably, the proposed framework is simple and generic, offering potential improvements across a broad spectrum of evolutionary algorithms. As a proof-of-principle study, we apply this framework to a group of differential evolution algorithms. The experimental results showcase the remarkable effectiveness of the proposed framework, not only enhancingthe overall optimization performance but also demonstrating favorable generalization ability across different problem classes.

关键词： Heuristic algorithms Optimization Statistics Sociology Trajectory Switches Search problems algorithm selection black-box optimization deep reinforcement learning differential evolution meta-black-box optimization

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Cascaded algorithm selection With Extreme-Region UCB Bandit

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6782-6794页

作者： Hu, Yi-Qi Liu, Xu-Hui Li, Shu-Qiao Yu, Yang Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Pazhou Lab Guangzhou 510330 Guangdong Peoples R China Polixir Ai Nanjing 210046 Jiangsu Peoples R China

AutoML aims at best configuring learning systems automatically. It contains core subtasks of algorithm selection and hyper-parameter tuning. Previous approaches considered searching in the joint hyper-parameter space of all algorithms, which forms a huge but redundant space and causes an inefficient search. We tackle this issue in a cascaded algorithm selection way, which contains an upper-level process of algorithm selection and a lower-level process of hyper-parameter tuning for algorithms. While the lower-level process employs an anytime tuning approach, the upper-level process is naturally formulated as a multi-armed bandit, deciding which algorithm should be allocated one more piece of time for the lower-level tuning. To achieve the goal of finding the best configuration, we propose the Extreme-Region Upper Confidence Bound (ER-UCB) strategy. Unlike UCB bandits that maximize the mean of feedback distribution, ER-UCB maximizes the extreme-region of feedback distribution. We first consider stationary distributions and propose the ER-UCB-S algorithm that has O(K ln n) regret upper bound with K arms and n trials. We then extend to non-stationary settings and propose the ER-UCB-N algorithm that has O(Kn(nu)) regret upper bound, where 2/3 < nu < 1. Finally, empirical studies on synthetic and AutoML tasks verify the effectiveness of ER-UCB-S/N by their outperformance in corresponding settings.

关键词： algorithm selection hyper-parameter tuning AutoML machine learning

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Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

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MACHINE LEARNING 2018年第1期107卷 247-283页

作者： Malone, Brandon Kangas, Kustaa Jarvisalo, Matti Koivisto, Mikko Myllymaki, Petri NEC Labs Europe Heidelberg Germany Univ Helsinki HIIT Dept Comp Sci Helsinki Finland

Various algorithms have been proposed for finding a Bayesian network structure that is guaranteed to maximize a given scoring function. Implementations of state-of-the-art algorithms, solvers, for this Bayesian network structure learning problem rely on adaptive search strategies, such as branch-and-bound and integer linear programming techniques. Thus, the time requirements of the solvers are not well characterized by simple functions of the instance size. Furthermore, no single solver dominates the others in speed. Given a problem instance, it is thus a priori unclear which solver will perform best and how fast it will solve the instance. We show that for a given solver the hardness of a problem instance can be efficiently predicted based on a collection of non-trivial features which go beyond the basic parameters of instance size. Specifically, we train and test statistical models on empirical data, based on the largest evaluation of state-of-the-art exact solvers to date. We demonstrate that we can predict the runtimes to a reasonable degree of accuracy. These predictions enable effective selection of solvers that perform well in terms of runtimes on a particular instance. Thus, this work contributes a highly efficient portfolio solver that makes use of several individual solvers.

关键词： Bayesian networks Structure learning algorithm selection Hyperparameter optimization Empirical hardness algorithm portfolio Runtime prediction

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The algorithm selection competitions 2015 and 2017

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ARTIFICIAL INTELLIGENCE 2019年 272卷 86-100页

作者： Lindauer, Marius van Rijn, Jan N. Kotthoff, Lars Univ Freiburg Freiburg Germany Columbia Univ New York NY 10027 USA Univ Wyoming Laramie WY 82071 USA

The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of Al. We report on the state of the art in algorithm selection, as defined by the algorithm selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art. (C) 2018 Elsevier B.V. All rights reserved.

关键词： algorithm selection Meta-Learning Competition Analysis

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