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Cascaded algorithm selection With Extreme-Region UCB Bandit

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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algorithm selection for Protein Structure Prediction on 2D AB Off-lattice Model 24

Algorithm Selection for Protein Structure Prediction on 2D A...

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Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics

作者： Mustafa Misir Duke Kunshan University Kunshan Jiangsu China

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Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

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ADVANCED ENGINEERING INFORMATICS 2022年 53卷

作者： Liu, Li Liu, Jian Zhou, Qichao Huang, De Liaoning Tech Univ Coll Safety Sci & Engn Huludao 125105 Peoples R China Minist Educ Key Lab Mine Thermo mot Disaster & Prevent Huludao 125105 Peoples R China Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Peoples R China Liaoning Tech Univ Huludao 125105 Liaoning Peoples R China

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.

关键词： Mine ventilation Fault diagnosis Machine learning algorithm selection Windage alteration fault

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A Feature-Free Approach to Automated algorithm selection

A Feature-Free Approach to Automated Algorithm Selection

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Genetic and Evolutionary Computation Conference (GECCO)

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

ISBN: (纸本)9798400701207

This article summarises recent work in the domain of feature-free algorithm selection that was published in the Journal of Heuristics in January 2023, with the title 'Automated algorithm selection: from Feature-Based to Feature-Free Approaches'. Specifically, we consider domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. We train two types of recurrent neural networks (RNN) to predict a packing heuristic in online bin-packing that use the sequence of item-sizes as input, i.e. no features are derived to describe the instance. This contrasts to typical approaches to algorithm-selection. 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. In order to explain the observed result, we suggest that our methods perform well when the instances exhibit some implicit structure. To provide evidence for this, 14 new datasets with controllable levels of structure are generated, indicating that a critical threshold of structure is required before algorithm-selection delivers benefit.

关键词： Deep Learning Recurrent Neural Network algorithm selection

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Dynamic Machine Learning algorithm selection For Network Slicing in Beyond 5G Networks 9

Dynamic Machine Learning Algorithm Selection For Network Sli...

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9th IEEE International Conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Bouroudi, Abdelmounaim Outtagarts, Abdelkader Hadjadj-Aoul, Yassine Nokia Networks France Bell Labs Massy France Univ Rennes INRIA CNRS IRISA Rennes France

ISBN: (纸本)9798350399806

The advanced 5G and 6G mobile network generations offer new capabilities that enable the creation of multiple virtual network instances with distinct and stringent requirements. However, the coexistence of multiple network functions on top of a shared substrate network poses a resource allocation challenge known as the Virtual Network Embedding (VNE) problem. In recent years, this NP-hard problem has received increasing attention in the literature due to the growing need to optimize resources at the edge of the network, where computational and storage capabilities are limited. In this demo paper, we propose a solution to this problem, utilizing the algorithm selection (AS) paradigm. This selects the most optimal Deep Reinforcement Learning (DRL) algorithm from a portfolio of agents, in an offline manner, based on past performance. To evaluate our solution, we developed a simulation platform using the OMNeT++ framework, with an orchestration module containerized using Docker. The proposed solution shows good performance and outperforms standalone algorithms.

关键词： Network Slicing Virtual Network Embedding Deep Reinforcement Learning algorithm selection Containerized Networks

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algorithm selection for Dynamic Symbolic Execution: A Preliminary Study 1

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30th International Conference on Logic-Based Program Synthesis and Transformation (LOPSTR)

作者： Amadini, Roberto Gange, Graeme Schachte, Peter Sondergaard, Harald Stuckey, Peter J. Univ Bologna Bologna Italy Univ Melbourne Parkville Vic Australia Monash Univ Clayton Vic Australia

ISBN: (数字)9783030684464

ISBN: (纸本)9783030684457;9783030684464

Given a portfolio of algorithms, the goal of algorithm selection (AS) is to select the best algorithm(s) for a new, unseen problem instance. Dynamic Symbolic Execution (DSE) brings together concrete and symbolic execution to maximise the program coverage. DSE uses a constraint solver to solve the path conditions and generate new inputs to explore. In this paper we join these lines of research by introducing a model that combines DSE and AS approaches. The proposed AS/DSE model is a generic and flexible framework enabling the DSE engine to solve the path conditions it collects with a portfolio of different solvers, by exploiting and extending the well-known AS techniques that have been developed over the last decade. In this way, one can increase the coverage and sometimes even outperform the aggregate coverage achievable by running simultaneously all the solvers of the portfolio.

关键词： Software verification Dynamic symbolic execution algorithm selection Constraint solving Portfolio solving

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Analyzing the Generalizability of Automated algorithm selection: A Case Study for Numerical Optimization

Analyzing the Generalizability of Automated Algorithm Select...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Skvorc, Urban Eftimov, Tome Korosec, Peter Jozef Stefan Institute Jozef Stefan International Postgraduate School Computer Systems Department Ljubljana Slovenia Jozef Stefan Institute Computer Systems Department Ljubljana Slovenia

ISBN: (纸本)9781665430654

In numerical single-objective optimization, auto-mated algorithm selection that uses exploratory landscape analy-sis to describe problem features has achieved great results when the machine learning models used for prediction are trained and tested on the same problem set. However, recent work has shown that the performance of such models decreases when the training and testing sets contain different problems. In this paper, we examine a recently developed algorithm selection model trained on a set of artificial problems and tested on a well-known set of hand-made benchmark problems. This model performed poorly when it was originally presented. Here, we provide an explanation for its poor performance by analyzing the feature importance of the model using Shapley Additive Explanations. We then compare these results to an alternative algorithm selection model that was both trained and tested on the same set of hand-made benchmark problems and achieved much higher performance. This allows us to determine which features each model considers as most significant for their predictions, and where they differ. We show that the original and the alternative model use different landscape features for their predictions, which explains the difference in their performance. Further, by plotting the SHAP values on a 2D plane, we show that the original model is unable to distinguish between certain types of problems. Finally, we show that regardless of their differences in utilizing the features both the original and the alternative models perform poorly on a specific group of problems. © 2023 IEEE.

关键词： algorithm selection exploratory landscape analysis numerical optimization

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algorithm selection as Superset Learning: Constructing algorithm Selectors from Imprecise Performance Data 1

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25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Hanselle, Jonas Tornede, Alexander Wever, Marcel Huellermeier, Eyke Paderborn Univ Heinz Nixdorf Inst Dept Comp Sci Paderborn Germany

ISBN: (数字)9783030757625

ISBN: (纸本)9783030757625;9783030757618

algorithm selection refers to the task of automatically selecting the most suitable algorithm for solving an instance of a computational problem from a set of candidate algorithms. Here, suitability is typically measured in terms of the algorithms' runtimes. To allow the selection of algorithms on new problem instances, machine learning models are trained on previously observed performance data and then used to predict the algorithms' performances. Due to the computational effort, the execution of such algorithms is often prematurely terminated, which leads to right-censored observations representing a lower bound on the actual runtime. While simply neglecting these censored samples leads to overly optimistic models, imputing them with precise though hypothetical values, such as the commonly used penalized average runtime, is a rather arbitrary and biased approach. In this paper, we propose a simple regression method based on so-called superset learning, in which right-censored runtime data are explicitly incorporated in terms of interval-valued observations, offering an intuitive and efficient approach to handling censored data. Benchmarking on publicly available algorithm performance data, we demonstrate that it outperforms the aforementioned naive ways of dealing with censored samples and is competitive to established methods for censored regression in the field of algorithm selection.

关键词： algorithm selection Superset learning Censored data

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A Novel RVFL-Based algorithm selection Approach for Software Model Checking 15th

A Novel RVFL-Based Algorithm Selection Approach for Software...

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15th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Cao, Weipeng Wu, Yuhao Wang, Qiang Zhang, Jiyong Zhang, Xingjian Qiu, Meikang Civil Aviat Univ China CAAC Key Lab Civil Aviat Wide Surveillance Safe operat Management & Control Technol Tianjin Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Chinese Acad Mil Sci Inst Syst Engn Beijing Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou Peoples R China Texas A&M Univ Commerce Dept Comp Sci Commerce TX USA

ISBN: (纸本)9783031109898;9783031109881

Software model checking is the technique that automatically verifies whether software meets the given correctness properties. In the past decades, a large number of model checking techniques and tools have been developed, reaching a point where modern model checkers are sophisticated enough to handle large-scale software systems. However, due to the fact that the software model checkering techniques are diverse and each of them is designed and optimized for a specific type of software system, it remains a hard problem for engineers to efficiently combine them to verify the complex software systems in practice. To alleviate this problem, we propose a novel algorithm selection approach based on Random Vector Functional Link net (RVFL) for software model checking, namely Kaleidoscopic RVFL (K-RVFL). The novel design of feature hybridization and fusion enables K-RVFL to extract more diverse and multi-level features. We have also carried out a thorough experimental evaluation on a publicly available data set and compared K-RVFL with a number of neural networks, including RVFL, Extreme Learning Machine (ELM), Stochastic Configuration Network (SCN), Back Propagation algorithm (BP), and Supporting Vector Machine (SVM). The experimental results demonstrate the usefulness and effectiveness of K-RVFL.

关键词： Software verification Software model checking algorithm selection Random vector functional link Neural networks

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Micro-MetaStream: algorithm selection for time-changing data

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INFORMATION SCIENCES 2021年 565卷 262-277页

作者： Debiaso Rossi, Andre Luis Soares, Carlos de Souza, Bruno Feres Ponce de Leon Ferreira de Carvalho, Andre Carlos Sao Paulo State Univ Unesp Campus Itapeva Itapeva SP Brazil Univ Porto Fac Engn Fraunhofer Portugal AICOS Porto Portugal Univ Porto Fac Engn LIAAD INESC TEC Porto Portugal Univ Fed Maranhao UFMA Sao Luis Maranhao Brazil Univ Sao Paulo Inst Ciencias Matemat & Comp Sao Carlos Brazil

Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level. (c) 2021 Elsevier Inc. All rights reserved.

关键词： algorithm selection Meta-learning Time-changing data

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