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18h ACM Conference on Recommender Systems (RecSys)

作者： Wegmeth, Lukas Vente, Tobias Beel, Joeran Univ Siegen Intelligent Syst Grp Siegen Germany

ISBN: (纸本)9798400705052

The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored. Traditional approaches in recommender systems algorithm selection focus predominantly on rating prediction on explicit feedback datasets, leaving a research gap for ranking prediction on implicit feedback datasets. algorithm selection is a critical challenge for nearly every practitioner in recommender systems. In this work, we take the first steps toward addressing this research gap. We evaluate the NDCG@10 of 24 recommender systems algorithms, each with two hyperparameter configurations, on 72 recommender systems datasets. We train four optimized machine-learning meta-models and one automated machine-learning meta-model with three different settings on the resulting meta-dataset. Our results show that the predictions of all tested meta-models exhibit a median Spearman correlation ranging from 0.857 to 0.918 with the ground truth. We show that the median Spearman correlation between meta-model predictions and the ground truth increases by an average of 0.124 when the meta-model is optimized to predict the ranking of algorithms instead of their performance. Furthermore, in terms of predicting the best algorithm for an unknown dataset, we demonstrate that the best optimized traditional meta-model, e.g., XGBoost, achieves a recall of 48.6%, outperforming the best tested automated machine learning meta-model, e.g., AutoGluon, which achieves a recall of 47.2%.

关键词： algorithm selection Automated Recommender Systems AutoRecSys Ranking Prediction Collaborative Filtering

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ADAPTIVE algorithm selection, WITH APPLICATIONS IN PEDESTRIAN DETECTION 23

ADAPTIVE ALGORITHM SELECTION, WITH APPLICATIONS IN PEDESTRIA...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Shu Zhu, Qi Roy-Chowdhury, Amit SONY Elect Inc San Jose CA 95112 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781467399616

Computer vision algorithms are known to be extremely sensitive to the environmental conditions in which the data is captured, e.g., lighting conditions and target density. Tuning of parameters or choosing a completely new algorithm is often needed to achieve a certain performance level. In this paper, we focus on this problem and propose a framework to automatically choose the "best" algorithm-parameter combination (often referred to as the best algorithm for simplicity in this paper) for a certain input data. This necessitates developing a mechanism to switch among different algorithms and parameters as the nature of the input video changes. Specifically, our proposed algorithm calculates a similarity function between a test video segment and a training video segment. Similarity between training and test dataset indicates the same algorithm can be applied to both of them. We design a cost function with this similarity measure and a constraint on the number of switches. In the experiments, we apply our algorithm to the problem of pedestrian detection. We show how to adaptively select among 7 algorithm-parameter combinations and provide promising results on 3 publicly available datasets.

关键词： algorithm selection adaptation pedestrian detection

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algorithm selection for solving educational timetabling problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114694-114694页

作者： de la Rosa-Rivera, Felipe Nunez-Varela, Jose I. Ortiz-Bayliss, Jose C. Terashima-Marin, Hugo Univ Autonoma San Luis Potosi Fac Ingn San Luis Potosi 78290 San Luis Potosi Mexico Tecnol Monterrey Escuela Ingn & Ciencias Nuevo Leon 64849 Mexico

In this paper, we present the construction process of a per-instance algorithm selection model to improve the initial solutions of Curriculum-Based Course Timetabling (CB-CTT) instances. Following the meta-learning framework, we apply a hybrid approach that integrates the predictions of a classifier and linear regression models to estimate and compare the performance of four meta-heuristics across different problem sub-spaces described by seven types of features. Rather than reporting the average accuracy, we evaluate the model using the closed SBS-VBS gap, a performance measure used at international algorithm selection competitions. The experimental results show that our model obtains a performance of 0.386, within the range obtained by perinstance algorithm selection models in other combinatorial problems. As a result of the process, we conclude that the performance variation between the meta-heuristics has a significant role in the effectiveness of the model. Therefore, we introduce statistical analyses to evaluate this factor within per-instance algorithm portfolios.

关键词： algorithm selection Meta-learning Educational timetabling Meta-heuristic

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algorithm selection using edge ML and case-based reasoning

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 162-162页

作者： Ali, Rahman Zada, Muhammad Sadiq Hassan Khatak, Asad Masood Hussain, Jamil Univ Peshawar Quaid E Azam Coll Commerce Peshawar Pakistan Univ Derby Kedleston Rd Derby England Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Sejong Univ Dept Data Sci Seoul South Korea

In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data sca

关键词： algorithm selection Machine learning Meta learning Edge ML Edge computing

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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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Application of Machine Learning to algorithm selection for TSP 26

Application of Machine Learning to Algorithm Selection for T...

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26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Pihera, Josef Musliu, Nysret Vienna Univ Technol Vienna PhD Sch Informat Vienna Austria Vienna Univ Technol Inst Informat Syst Vienna Austria

ISBN: (纸本)9781479965731

The Travelling Salesman Problem (TSP) has been extensively studied in the literature and various solvers are available. However, none of the state-of-the-art solvers for TSP outperforms the others in all problem instances within a given time limit. Therefore, the prediction of the best performing algorithm can save computational resources and optimise the results. In this paper, the TSP is studied in context of automated algorithm selection. Our aim is to identify the relevant features of problem instances and tackle this scenario as a machine learning task. We extend the set of existing features in the literature and propose several novel features to better characterise the problem. The contribution of the new features is statistically analysed and experiments show that adding our new features improves the prediction accuracy. We identified that our features based on kNN graph transformation are especially helpful. To create the training datasets, two state-of-the-art (meta-) heuristic algorithms are systematically evaluated on more than 2000 problems. Overall, we show that our prediction can be substantially more accurate than simple preference of an algorithm with the best performance for a majority of problem instances.

关键词： TSP algorithm selection features machine learning

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Analyzing Data Complexity Using Metafeatures for Classification algorithm selection

Analyzing Data Complexity Using Metafeatures for Classificat...

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Shah, Rushit N. Khemani, Varun Azarian, Michael H. Pecht, Michael G. Su, Yan Univ Maryland CALCE College Pk MD 20740 USA Nanjing Univ Aeronaut & Astronaut Civil Aviat Coll Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9781538653807

Supervised machine learning is defined as the task of seeking algorithms which, based on reasoning gained from historical data, produce a general hypothesis, can then be used to infer knowledge about new data in the future. Classification, an instance of supervised learning, is statistically defined as the problem of identifying to which particular subpopulation a new observation of data belongs. Given the large number of available classification algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the best method to analyze a new data set has become an ever more challenging task. Typically, finding the best classifier for a given data set involves empirically iterating through all candidate classifiers and choosing the one which provides the best classification accuracies. Clearly, this task is computationally very expensive and the computation cost increases with the addition of each new candidate algorithm. This problem is compounded by the fact that there is not adequate generalizability of these classification methods over data sets from different domains. For example, classifier performance obtained with medical imaging data may not hold for financial data when trying to achieve a similar classification task using the same classifier. How, then, does one efficiently choose a classifier which will provide better classification performance than others? In this context, this study aims to streamline the task of algorithm selection for classification using the meta-learning framework. We propose a methodology to analyze empirically a set of measures of data complexity, known as metafeatures, and investigate their influence on the classification performance of several widely used classifiers. Doing so allows a map of a performance metric to be generated over the metafeature space of data sets. This map is partitioned into regions where some classifiers perform better than others. Once implemented, a new data set can be located in

关键词： metafeatures algorithm selection data complexity

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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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R-CASS: Using algorithm selection for Self-Adaptive Service Oriented Systems

R-CASS: Using Algorithm Selection for Self-Adaptive Service ...

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IEEE International Conference on Web Services (ICWS) / IEEE World Congress on Services (IEEE SERVICES)

作者： Deshpande, Niranjana Sharma, Naveen Yu, Qi Krutz, Daniel E. Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Rochester NY 14623 USA

ISBN: (纸本)9781665416818

In service composition, complex applications are built by combining web services to fulfill user Quality of Service (QoS) and business requirements. To meet these requirements, applications are composed by evaluating all possible web service combinations using search algorithms. These algorithms need to be accurate and inexpensive to evaluate a large number of possible service combinations and services' fluctuating QoS attributes while meeting the constraints of limited computational resources. Recent research has shown that different search algorithms can outperform others on specific instances of a problem domain, in terms of solution quality and computational resource usage. Problematically, current service composition approaches ignore this property, leading to inefficient compositions. To address these limitations, we propose a composition algorithm selection framework which selects an algorithm per composition task at runtime, R-CASS. Our evaluations demonstrate that R-CASS leads to more efficient compositions, reducing composition time by 55.1% and memory by 37.5%.

关键词： Service Composition algorithm selection Runtime Adaptation MAPE-K Composition algorithm Adaptation Supervised Learning

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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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