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Asian Conference on Machine Learning

作者： Tornede, Alexander Wever, Marcel Werner, Stefan Mohr, Felix Huellermeier, Eyke Paderborn Univ Heinz Nixdorf Inst Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany Univ La Sabana Chia Cundinamarca Colombia

algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.

关键词： algorithm selection survival analysis risk-aversion surrogate loss

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Attend and Represent: A Novel View on algorithm selection for Software Verification 35

Attend and Represent: A Novel View on Algorithm Selection fo...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Richter, Cedric Wehrheim, Heike Paderborn Univ Paderborn Germany

ISBN: (纸本)9781450367684

Today, a plethora of different software verification tools exist. When having a concrete verification task at hand, software developers thus face the problem of algorithm selection. Existing algorithm selectors for software verification typically use handpicked program features together with (1) either manually designed selection heuristics or (2) machine learned strategies. While the first approach suffers from not being transferable to other selection problems, the second approach lacks interpretability, i.e., insights into reasons for choosing particular tools. In this paper, we propose a novel approach to algorithm selection for software verification. Our approach employs representation learning together with an attention mechanism. Representation learning circumvents feature engineering, i.e., avoids the handpicking of program features. Attention permits a form of interpretability of the learned selectors. We have implemented our approach and have experimentally evaluated and compared it with existing approaches. The evaluation shows that representation learning does not only outperform manual feature engineering, but also enables transferability of the learning model to other selection tasks.

关键词： Software verification algorithm selection representation learning attention

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A Survey of Personalized Recommendation algorithm selection Based on Meta-learning

A Survey of Personalized Recommendation Algorithm Selection ...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Ren, Yi Chi, Cuirong Zhang Jintao Shenyang Jianzhu Univ Shenyang Liaoning Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The purpose of algorithm selection is to automatically select the most suitable one for the current problem from among the many available optimization algorithms. For this problem, this paper describes the recommendation algorithm selection of meta-learning. Firstly, the related techniques based on meta-learning are analyzed. Then the algorithm selection framework and the extraction of meta-features are constructed. Finally, the problems of algorithm selection based on meta-learning ideas are proposed, and the future development direction is pointed out.

关键词： algorithm selection Meta-learning Meta-feature

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A Distance-Weighted Class-Homogeneous Neighbourhood Ratio for algorithm selection 12

A Distance-Weighted Class-Homogeneous Neighbourhood Ratio fo...

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Asian Conference on Machine Learning

作者： Chen, Haofei Liu, Ya Ahuja, Japnit Kaur Ler, Daren Natl Jr Coll 37 Hillcrest Rd Singapore 288913 Singapore Natl Univ Singapore 21 Lower Kent Ridge Rd Singapore 119077 Singapore

In this paper, we introduce a new form of meta-feature that is based on a distance-weighted class-homogeneous neighbourhood ratio to facilitate algorithm selection. We show that these new meta-features, while exhibiting a cost advantage, achieve a comparable, and in some cases, higher performance than conventional meta-features. These results were obtained via experiments conducted over artificial datasets and real-world datasets from the UCI repository. We further redefine the algorithm selection problem by advocating that accuracy should be calculated based on the assumption that the population of datasets is uniformly distributed. Finally, in this paper, we provide a new perspective on landmarkers, such that a landmarker corresponds to a tuple (algorithm, metric), and propose the idea of a new family of meta-features.

关键词： Meta-learning algorithm selection Meta-features Landmarking AutoML

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Hybrid Ranking and Regression for algorithm selection 43rd

Hybrid Ranking and Regression for Algorithm Selection

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43rd German Conference on Artificial Intelligence (KI)

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

ISBN: (纸本)9783030582845;9783030582852

algorithm selection (AS) is defined as the task of automatically selecting the most suitable algorithm from a set of candidate algorithms for a specific instance of an algorithmic problem class. While suitability may refer to different criteria, runtime is of specific practical relevance. Leveraging empirical runtime information as training data, the AS problem is commonly tackled by fitting a regression function, which can then be used to estimate the candidate algorithms' runtimes for new problem instances. In this paper, we develop a new approach to algorithm selection that combines regression with ranking, also known as learning to rank, a problem that has recently been studied in the realm of preference learning. Since only the ranking of the algorithms is eventually needed for the purpose of selection, the precise numerical estimation of runtimes appears to be a dispensable and unnecessarily difficult problem. However, discarding the numerical runtime information completely seems to be a bad idea, as we hide potentially useful information about the algorithms' performance margins from the learner. Extensive experimental studies confirm the potential of our hybrid approach, showing that it often performs better than pure regression and pure ranking methods.

关键词： algorithm selection Hybrid loss optimization Combined ranking and regression

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algorithm selection Using Deep Learning Without Feature Extraction

Algorithm Selection Using Deep Learning Without Feature Extr...

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Genetic and Evolutionary Computation Conference (GECCO)

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

ISBN: (纸本)9781450361118

We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In contrast to the majority of work in algorithm-selection, the approach does not need any features to be extracted from the data but instead relies on the temporal data sequence as input. A large case-study in the domain of 1-d bin packing is undertaken in which instances can be solved by one of four heuristics. We first evolve a large set of new problem instances that each have a clear "best solver" in terms of the heuristics considered. An RNN-LSTM is trained directly using the sequence data describing each instance to predict the best-performing heuristic. Experiments conducted on small and large problem instances with item sizes generated from two different probability distributions are shown to achieve between 7% to 11% improvement over the single best solver (SBS) (i.e. the single heuristic that achieves the best performance over the instance set) and 0% to 2% lower than the virtual best solver (VBS), i.e the perfect mapping.

关键词： Deep Learning Recurrent Neural Network algorithm selection

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Sampling Effects on algorithm selection for Continuous Black-Box Optimization

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algorithmS 2021年第1期14卷

作者： Munoz, Mario Andres Kirley, Michael Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia Univ Melbourne Sch Comp & Informat Syst Parkville Vic 3010 Australia

In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which uses exploratory landscape analysis features as inputs. We test the accuracy of the recommendations experimentally using resampling techniques and the hold-one-instance-out and hold-one-problem-out validation methods. The results demonstrate that the selector remains accurate even with sampling noise, although not without trade-offs.

关键词： algorithm selection black-box optimization single-objective continuous optimization exploratory landscape analysis performance prediction randomized heuristics

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Graph Convolutional Network Based Generative Adversarial Networks for the algorithm selection Problem in Classification 20

Graph Convolutional Network Based Generative Adversarial Net...

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Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System

作者： Gleb Drozdov Alexey Zabashta Andrey Filchenkov ITMO University Russia

ISBN: (纸本)9781450388054

In this work, we address the algorithm selection problem for classification via meta-learning and generative adversarial networks. We focus on the dataset representation question. The matrix representation of classification dataset is not sensitive to swapping any two rows or any two columns. We suggest a special method to reduce a dataset to a unified form. This allows to apply generative adversarial networks to classification dataset generation. In this setting, a generator generates new classification datasets in a matrix form, while a conditional discriminator is trying to predict for a dataset and an algorithm if the dataset is real and the algorithm would show the best performance on this dataset. We also suggest a graph convolutional network as a discriminator that is capable to work with such forms, which encode a dataset as a weighted graph with nodes representing objects.

关键词： Generative adversarial nets Graph convolutional network Dataset representation Dataset synthesis Machine learning Meta-learning algorithm selection

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Attend and represent: a novel view on algorithm selection for software verification 20

Attend and represent: a novel view on algorithm selection fo...

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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Cedric Richter Heike Wehrheim Paderborn University Paderborn Germany

ISBN: (纸本)9781450367684

Today, a plethora of different software verification tools exist. When having a concrete verification task at hand, software developers thus face the problem of algorithm selection. Existing algorithm selectors for software verification typically use handpicked program features together with (1) either manually designed selection heuristics or (2) machine learned strategies. While the first approach suffers from not being transferable to other selection problems, the second approach lacks interpretability, i.e., insights into reasons for choosing particular *** this paper, we propose a novel approach to algorithm selection for software verification. Our approach employs representation learning together with an attention mechanism. Representation learning circumvents feature engineering, i.e., avoids the handpicking of program features. Attention permits a form of interpretability of the learned selectors. We have implemented our approach and have experimentally evaluated and compared it with existing approaches. The evaluation shows that representation learning does not only outperform manual feature engineering, but also enables transferability of the learning model to other selection tasks.

关键词： representation learning software verification algorithm selection attention

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Topic specificity: A descriptive metric for algorithm selection and finding the right number of topics

Natural Language Processing Journal

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Natural Language Processing Journal 2024年 8卷

作者： Emil Rijcken Kalliopi Zervanou Pablo Mosteiro Floortje Scheepers Marco Spruit Uzay Kaymak Jheronimus Academy of Data Science Eindhoven University of Technology ’s Hertogenbosch The Netherlands Department of Information and Computing Sciences Utrecht University Utrecht The Netherlands Department of Methodology and Statistics Utrecht University Utrecht The Netherlands Psychiatry University Medical Center Utrecht Utrecht The Netherlands Leiden Institute of Advanced Computer Science Leiden The Netherlands Public Health & Primary Care Leiden University Medical Center Leiden The Netherlands

Topic modeling is a prevalent task for discovering the latent structure of a corpus, identifying a set of topics that represent the underlying themes of the documents. Despite its popularity, issues with its evaluation metric, the coherence score, result in two common challenges: algorithm selection and determining the number of topics . To address these two issues, we propose the topic specificity metric, which captures the relative frequency of topic words in the corpus and is used as a proxy for the specificity of a word. In this work, we formulate the metric firstly. Secondly, we demonstrate that algorithms train topics at different specificity levels. This insight can be used to address algorithm selection as it allows users to distinguish and select algorithms with the desired specificity level. Lastly, we show a strictly positive monotonic correlation between the topic specificity and the number of topics for LDA, FLSA-W, NMF and LSI. This correlation can be used to address the selection of the number of topics, as it allows users to adjust the number of topics to their desired level. Moreover, our descriptive metric provides a new perspective to characterize topic models, allowing them to be understood better.

关键词： Topic modeling Metric algorithm selection

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