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检索条件"主题词=algorithm selection"

共 313 条记录，以下是251-260 订阅

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Selecting algorithms for the Quadratic Assignment Problem with a Multi-label Meta-learning Approach

Selecting Algorithms for the Quadratic Assignment Problem wi...

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7th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Dantas, Augusto Lopez Ramirez Pozo, Aurora Trinidad Univ Fed Parana Dept Comp Sci Curitiba Parana Brazil

ISBN: (纸本)9781538680230

Meta-heuristic algorithms have been used to obtain feasible solutions in reasonable time for many NP-hard search problems. However, the performance of the algorithms heavily depends on the features of the problem. So, techniques for automatically choosing or designing algorithms have received attention from researchers in the past years. In this research, we investigate automated algorithm selection for the Quadratic Assignment Problem using a multi-label meta-learning approach. In multi-label learning, an instance can have more than one label at the same time, which is an adequate configuration for this scenario, since there can be ties in the performances of the algorithms. Two approaches for dealing with multi-label learning are compared, regarding both the prediction of which algorithms are the best and the resulting costs of the solutions. The experiments show that the algorithm selection process can yield better results than if we choose only one single algorithm to solve all instances.

关键词： algorithm selection multi-label learning combinatorial optimization

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An experimental evaluation of data mining algorithms using hyperparameter optimization 14

An experimental evaluation of data mining algorithms using h...

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14th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Marques, Rayrone Z. N. Coutinho, Luciano R. Borchartt, Tiago B. Vale, Samyr B. Silva, Francisco J. S. Univ Fed Maranhao Dept Informat Postgrad Program Comp Sci Sao Luis MA Brazil

ISBN: (纸本)9781509003235

The challenge to choose the best algorithm and its best parameters for a given problem is known as Combined algorithm selection and Hyperparameter Optimization Problem. Among all the classification algorithms available are those based on human comprehensible representations, such as decision trees and classification rule induction. These algorithms are usually chosen by the clarity of the results obtained and the interpretability of its models. In this paper, we evaluated the six most used algorithms based on human comprehension. We conducted experiments with 28 datasets often used in the literature in different ways: using default parameters, using ExpDB parameters and using a tool based in genetic algorithm to find the best parameter combination. The results obtained have shown the strategy of combining the data from ExpDB via GA is effective in finding classification models with good accuracy.

关键词： Data mining Decision Tree algorithm selection Parameter Optimization Genetic algorithms

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Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization 20

Auto-Surprise: An Automated Recommender-System (AutoRecSys) ...

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14th ACM Conference on Recommender Systems (RECSYS)

作者： Anand, Rohan Beel, Joeran Trinity Coll Dublin Dublin Ireland

ISBN: (纸本)9781450375832

We introduce Auto-Surprise(1), an automated recommender system library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to an out-of-the-box Surprise library, without hyper parameter optimization, AutoSurprise performs better, when evaluated with MovieLens, Book Crossing and Jester datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.

关键词： AutoRecSys AutoML algorithm selection hyperparameter optimization

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Improving Recommender Systems Through the Automation of Design Decisions 23

Improving Recommender Systems Through the Automation of Desi...

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17th ACM Conference on Recommender Systems (RecSys)

作者： Wegmeth, Lukas Univ Siegen Siegen Germany

ISBN: (纸本)9798400702419

Recommender systems developers are constantly faced with difficult design decisions. Additionally, the number of options that a recommender systems developer has to consider continually grows over time with new innovations. The machine learning community is in a similar situation and has come together to tackle the problem. They invented concepts and tools to make machine learning development both easier and faster. These developments are categorized as automated machine learning (AutoML). As a result, the AutoML community formed and continuously innovates new approaches. Inspired by AutoML, the recommender systems community has recently understood the need for automation and sparsely introduced AutoRecSys. The goal of AutoRecSys is not to replace recommender systems developers but to improve performance through the automation of design decisions. With AutoRecSys, recommender systems engineers do not have to focus on easy but time-consuming tasks and are free to pursue difficult engineering tasks instead. Additionally, AutoRecSys enables easier access to recommender systems for beginners as it reduces the amount of knowledge required to get started with the development of recommender systems. AutoRecSys, like AutoML, is still early in its development and does not yet cover the whole development pipeline. Additionally, it is not yet clear, under which circumstances AutoML approaches can be transferred to recommender systems. Our research intends to close this gap by improving AutoRecSys both with regard to the transfer of AutoML and novel approaches. Furthermore, we focus specifically on the development of novel automation approaches for data processing and training. We note that the realization of AutoRecSys is going to be a community effort. Our part in this effort is to research AutoRecSys fundamentals, build practical tools for the community, raise awareness of the advantages of automation, and catalyze AutoRecSys development.

关键词： recommender systems automation autorecsys automl optimization algorithm selection

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Towards Automated Configuration of Stream Clustering algorithms 19th

Towards Automated Configuration of Stream Clustering Algorit...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Carnein, Matthias Trautmann, Heike Bifet, Albert Pfahringer, Bernhard Univ Munster Munster Germany Univ Waikato Hamilton New Zealand

ISBN: (纸本)9783030438234;9783030438227

Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today's data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.

关键词： Stream clustering Automated algorithm configuration algorithm selection Ensemble techniques

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Performance Issues in Evaluating Models and Designing Simulation algorithms

Performance Issues in Evaluating Models and Designing Simula...

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International Workshop on High Performance Computational Systems Biology (HiBi 2009)

作者： Ewald, Roland Himmelspach, Jan Jeschke, Matthias Leye, Stefan Uhrmacher, Adelinde M. Univ Rostock Inst Comp Sci Rostock Germany

ISBN: (纸本)9780769538099

The increase and diversity of simulation methods bears witness of the need for more efficient discrete event simulations in computational biology - but how efficient are those methods. and how to ensure all efficient simulation for a concrete model? As the performance of simulation methods depends oil the model, the simulator. and the infrastructure, general answers to those questions are likely to remain illusive;they have to be sought individually and experimentally instead. This requires configurable implementations of many algorithms, means to define and conduct meaningful experiments on them, and mechanisms for storing and analyzing observed performance data. In this paper, we first overview basic approaches for improving simulation performance and illustrate the challenges when comparing different methods. We then argue that providing all the aforementioned components in a single tool, in our case the open source modeling and simulation framework JAMES II, reveals many synergies in effectively pursuing both questions. This is exemplified by presenting results of recent studies and introducing a new component to swiftly evaluate simulator code changes against previous experimental data.

关键词： Stochastic Simulation Experimental algorithmics Performance Evaluation algorithm selection Distributed Simulaton SSA Next Sub-volume Method Machine Learning

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Improved Automated CASH Optimization with Tree Parzen Estimators for Class Imbalance Problems 8

Improved Automated CASH Optimization with Tree Parzen Estima...

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8th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Duc Anh Nguyen Kong, Jiawen Wang, Hao Menzel, Stefan Sendhoff, Bernhard Kononova, Anna, V Baeck, Thomas Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands Honda Res Inst Europe GmbH HRI EU Offenbach Germany

ISBN: (纸本)9781665420990

The imbalanced classification problem is very relevant in both academic and industrial applications. The task of finding the best machine learning model to use for a specific imbalanced dataset is complicated due to a large number of existing algorithms, each with its own hyperparameters. The Combined algorithm selection and Hyperparameter optimization (CASH) has been introduced to tackle both aspects at the same time. However, CASH has not been studied in detail in the class imbalance domain, where the best combination of resampling technique and classification algorithm is searched for, together with their optimized hyperparameters. Thus, we target the CASH problem for imbalanced classification. We experiment with a search space of 5 classification algorithms, 21 resampling approaches and 64 relevant hyperparameters in total. Moreover, we investigate performance of 2 well-known optimization approaches: Random search and Tree Parzen Estimators approach which is a kind of Bayesian optimization. For comparison, we also perform grid search on all combinations of resampling techniques and classification algorithms with their default hyperparameters. Our experimental results show that a Bayesian optimization approach outperforms the other approaches for CASH in this application domain.

关键词： hyperparameter optimization algorithm selection imbalanced classification CASH optimization

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Benchmarking algorithm Portfolio Construction Methods 22

Benchmarking Algorithm Portfolio Construction Methods

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Munoz, Mario Andres Soleimani, Hamed Kandanaarachchi, Sevvandi Univ Melbourne Sch Math & Stat Parkville Vic Australia RMIT Univ Sch Sci Melbourne Vic Australia

ISBN: (纸本)9781450392686

A portfolio is a set of algorithms, which run concurrently or interchangeably, whose aim is to improve performance by avoiding a bad selection of a single algorithm. Despite its high error tolerance, a carefully constructed portfolio, i.e., the smallest set of complementary algorithms, is expected to perform better than an arbitrarily constructed one. In this paper, we benchmark five algorithm portfolio construction methods, using as benchmark problems the ASLib scenarios, under a cross-validation regime. We examine the performance of each portfolio in terms of its riskiness, i.e., the existence of unsolved problems on the test set, and its robustness, i.e., the existence of an algorithm that solves most instances. The results demonstrate that two of these methods produce portfolios with the lowest risk, albeit with different levels of robustness.

关键词： algorithm Portfolios algorithm selection Benchmarking

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One-Class Classification for Selecting Synthetic Datasets in Meta-Learning

One-Class Classification for Selecting Synthetic Datasets in...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Parente, Regina R. Prudencio, Ricardo B. C. Univ Fed Pernambuco UFPE Ctr Informat CIn Recife PE Brazil

ISBN: (纸本)9781728169262

algorithm selection is a challenging task in machine learning. Meta-learning treats algorithm selection as a supervised learning task, in which training examples (i.e., meta examples) are generated from experiments performed with a set of candidate algorithms in several datasets. The small availability of real datasets in some domains can make it difficult to generate good sets of meta-examples. An alternative is the use of synthetic datasets. Unfortunately, not all synthetic datasets can be considered equally relevant and representative compared to real datasets. Thus simply adopting a high number of arbitrary synthetic datasets increases the computational cost of performing experiments, without necessarily improving the quality of meta learning. In this paper, we treat the selection of relevant synthetic datasets for meta-learning as an One-Class Classification (OCC) problem. In this problem, it is assumed the availability of instances associated to a single class of interest (the positive class) and a large set of unlabelled instances (the unknown class). The objective is to classify which unlabelled instances most likely belong to the positive class. In our context, OCC techniques are used to select the most relevant synthetic datasets (unknown class), by considering the real datasets (positive class) available. In our work, we conducted experiments in a case study in which we adopted a data manipulation procedure to produce synthetic datasets and two OCC techniques for dataset selection. The results revealed that it was actually possible to select a reduced number of synthetic datasets while maintaining or even increasing meta-learning performance.

关键词： meta-learning algorithm selection one-class classification

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Using Result Profiles to Drive Meta-learning 18th

Using Result Profiles to Drive Meta-learning

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18th European, Mediterranean, and Middle Eastern Conference on Information Systems (EMCIS)

作者： Grabczewski, Krzysztof Nicolaus Copernicus Univ Inst Engn & Technol Fac Phys Astron & Informat Ul Grudziadzka 5 PL-87100 Torun Poland

ISBN: (纸本)9783030959470;9783030959463

Knowledge gained by meta-learning processes is valuable when it can be successfully used in solving algorithm selection problems. There is still strong need for automated tools for learning from data, performing model construction and selection with little or no effort from human operator. This article provides evidence for efficacy of a general meta-learning algorithm performing validations of candidate learning methods and driving the search for most attractive models on the basis of an analysis of learning results profiles. The profiles help in finding similar processes performed for other datasets and pointing to promising learning machines configurations. Further research on profile management is expected to bring very attractive automated tools for learning from data. Here, several components of the framework have been examined and an extended test performed to confirm the possibilities of the method. The discussion also touches on the subject of testing and comparing the results of meta-learning algorithms.

关键词： Meta-learning Meta-search algorithm selection algorithm ranking Result profiles

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