检索结果-内蒙古大学图书馆

Pairwise meta-rules for better meta-learning-based algorithm ranking

MACHINE LEARNING 2013年第1期93卷 141-161页

作者： Sun, Quan Pfahringer, Bernhard Univ Waikato Dept Comp Sci Hamilton New Zealand

In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset.

关键词： Meta-learning algorithm ranking ranking trees Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods

引用

INFORMATION SCIENCES 2015年 301卷 181-194页

作者： Ferrari, Daniel Gomes de Castro, Leandro Nunes Univ Prebiteriana Mackenzie Nat Comp Lab LCoN Sao Paulo Brazil

Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the algorithm Selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning;that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects;and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Clustering Problem characterization algorithm ranking algorithm selection Meta-knowledge Meta-learning systems

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic algorithm Similarity Analysis Based on Performance Metric Mapping of Fractional ranking 9

Metaheuristic Algorithm Similarity Analysis Based on Perform...

引用

9th IEEE International Conference on Information and Automation for Sustainability (ICIAfS)

作者： Zhang, Yong-Wei Halgamuge, Saman K. Jiangsu Univ Sci & Technol Coll Elect & Informat Zhenjiang Jiangsu Peoples R China Australian Natl Univ Res Sch Engn Coll Engn & Comp Sci Canberra ACT Australia Univ Melbourne Dept Mech Engn Sch Elect Mech & Infrastruct Engn Parkville Vic Australia

ISBN: (纸本)9781538694183

Although the diversity of metaheuristic algorithms has been frequently highlighted, the similarity of these algorithms is not studied comprehensively. This work studies the similarity of metahruristic algorithms from their performance perspective captured in a newly proposed fractional ranking method, which can map comprehensive performance measures into a scalar framework. The fractional ranking data is clustered using a k-medoids clustering to find similarities between algorithms. Results show that the proposed similarity analysis scheme reveals a new perspective of metaheuristic algorithms.

关键词： metaheuristic algorithm algorithm similarity clustering fractional ranking evolutionary algorithm algorithm evaluation algorithm ranking distance measure clustering validity

来源：评论

学校读者我要写书评

暂无评论

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms

引用

Building Simulation 2022年第5期15卷 817-830页

作者： Mo Li Fei Li Yuanqi Jing Kai Zhang Hao Cai Lufang Chen Xian Zhang Lihang Feng College of Urban Construction Nanjing Tech UniversityNanjing210009China Institute of Defense Engineering Academy of Military SciencePLABeijing100850China College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing210009Chino

Effective identification of pollution sources is particularly important for indoor air *** estimation of source strength is the basis for source effective *** paper proposes an optimization method for the deconvolution process in the source strength inverse *** the scheme,the concept of time resolution was defined,and combined with different filtering positions and filtering *** measures to reduce effects of measurement noise were quantitatively ***,the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and *** hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative *** showed that for the filtering position and algorithm,Butterworth filtering performed better,and different filtering positions had little effect on the inverse *** the calculation time step,the optimal Tr(time resolution)was 0.667%and 1.33%in the simulation and experiment,*** hybrid algorithms were found to not perform better than the single algorithms,and the SART(simultaneous algebraic reconstruction technique)algorithm from CAT(computer assisted tomography)yielded better performances in the accuracy and stability of source strength *** relative errors of the inverse calculation for source strength were typically below 25%using the optimization scheme.

关键词： pollutant source measurement noise inverse algorithm indoor air algorithm ranking

来源：评论

学校读者我要写书评

暂无评论

Automatic selection of clustering algorithms using supervised graph embedding

引用

INFORMATION SCIENCES 2021年 577卷 824-851页

作者： Cohen-Shapira, Noy Rokach, Lior Ben Gurion Univ Negev Beer Sheva Israel

The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led to increased interest in automated ML solutions that reduce the need for human intervention. One of the main challenges in applying ML to previously unseen problems is algorithm selection - the identification of high-performing algorithm(s) for a given dataset, task, and evaluation measure. This study addresses the algorithm selection challenge for data clustering, a fundamental task in data mining that is aimed at grouping similar objects. We present MARCO-GE, a novel meta-learning approach for the automated recommendation of clustering algorithms. MARCO-GE first transforms datasets into graphs and then utilizes a graph convolutional neural network technique to extract their latent representation. Using the embedding representations obtained, MARCO-GE trains a ranking meta-model capable of accurately recommending top-performing algorithms for a new dataset and clustering evaluation measure. An extensive evaluation on 210 datasets, 17 clustering algorithms, and 10 clustering measures demonstrates the effectiveness of our approach and its superiority in terms of predictive and generalization performance over state-of-the-art clustering meta-learning approaches. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Meta-learning algorithm selection Clustering AutoML algorithm ranking

来源：评论

学校读者我要写书评

暂无评论

Architecture Analysis and Benchmarking of 3D U-Shaped Deep Learning Models for Thoracic Anatomical Segmentation

引用

IEEE ACCESS 2024年 12卷 127592-127603页

作者： Harirpoush, Arash Rasoulian, Amirhossein Kersten-Oertel, Marta Xiao, Yiming Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada NeuroRx Res Montreal PQ H2X 3P9 Canada

Recent rising interests in patient-specific thoracic surgical planning and simulation require efficient and robust creation of digital anatomical models from automatic medical image segmentation algorithms. Deep learning (DL) is now state-of-the-art in various radiological tasks, and U-shaped DL models have particularly excelled in medical image segmentation since the inception of the 2D UNet. To date, many variants of U-shaped models have been proposed by the integration of different attention mechanisms and network configurations. Systematic benchmark studies which analyze the architecture of these models by leveraging the recent development of the multi-label databases, can provide valuable insights for clinical deployment and future model designs, but such studies are still rare. We conduct the first systematic benchmark study for variants of 3D U-shaped models (3DUNet, STUNet, AttentionUNet, SwinUNETR, FocalSegNet, and a novel 3D SwinUnet with four variants) with a focus on CT-based anatomical segmentation for thoracic surgery. Our study systematically examines the impact of different attention mechanisms, the number of resolution stages, and network configurations on segmentation accuracy and computational complexity. To allow cross-reference with other recent benchmarking studies, we also included a performance assessment of the BTCV abdominal structural segmentation. With the STUNet ranking at the top, our study demonstrated the value of CNN-based U-shaped models for the investigated tasks and the benefit of residual blocks in network configuration designs to boost segmentation performance. The code and trained model weights from the study can be found at https://***/HealthX-Lab/DeepSegThoracic.

关键词： Surgery Image segmentation Computational modeling Three-dimensional displays Solid modeling Benchmark testing Accuracy algorithm ranking anatomical segmentation benchmarking computed tomography deep learning thoracic surgery U-shaped models

来源：评论

学校读者我要写书评

暂无评论

A Test for FLOPs as a Discriminant for Linear Algebra algorithms 34

A Test for FLOPs as a Discriminant for Linear Algebra Algori...

引用

34th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Sankaran, Aravind Bientinesi, Paolo RWIH Aachen Univ IRTG MIP Aachen Germany Umea Univ Dept Comp Sci Umea Sweden

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Linear algebra expressions, which play a central role in countless scientific computations, are often computed via a sequence of calls to existing libraries of building blocks (such as those provided by BLAS and LAPACK). A sequence identifies a computing strategy, i.e., an algorithm, and normally for one linear algebra expression many alternative algorithms exist. Although mathematically equivalent, those algorithms might exhibit significant differences in terms of performance. Several high-level languages and tools for matrix computations such as Julia, Armadillo, Linnea, etc., make algorithmic choices by minimizing the number of Floating Point Operations (FLOPs). However, there can be several algorithms that share the same (or have nearly identical) number of FLOPs;in many cases, these algorithms exhibit execution times which are statistically equivalent and one could arbitrarily select one of them as the best algorithm. It is however not unlikely to find cases where the execution times are significantly different from one another (despite the FLOP count being almost the same). It is also possible that the algorithm that minimizes FLOPs is not the one that minimizes execution time. In this work, we develop a methodology to test the reliability of FLOPs as discriminant for linear algebra algorithms. Given a set of algorithms (for an instance of a linear algebra expression) as input, the methodology ranks them into performance classes;i.e., multiple algorithms are allowed to share the same rank. To this end, we measure the algorithms iteratively until the changes in the ranks converge to a value close to zero. FLOPs are a valid discriminant for an instance if all the algorithms with minimum FLOPs are assigned the best rank;otherwise, the instance is regarded as an anomaly, which can then be used in the investigation of the root cause of performance differences.

关键词： Performance Analysis Linear algebra algorithms algorithm ranking Mathematical software performance

来源：评论

学校读者我要写书评

暂无评论

Using Result Profiles to Drive Meta-learning 18th

Using Result Profiles to Drive Meta-learning

引用

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

来源：评论

学校读者我要写书评

暂无评论

Performance Comparison for Scientific Computations on the Edge via Relative Performance

Performance Comparison for Scientific Computations on the Ed...

引用

35th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Sankaran, Aravind Bientinesi, Paolo Rhein Westfal TH Aachen IRTG Modern Inverse Problems Aachen Germany Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9781665435772

In a typical Internet-of-Things setting that involves scientific applications, a target computation can be evaluated in many different ways depending on the split of computations among various devices. On the one hand, different implementations (or algorithms)-equivalent from a mathematical perspective-might exhibit significant difference in terms of performance. On the other hand, some of the implementations are likely to show similar performance characteristics. In this paper, we focus on analysing the performance of a given set of algorithms by clustering them into performance classes. To this end, we use a measurement-based approach to evaluate and score algorithms based on pair-wise comparisons;we refer to this approach as "Relative performance analysis". Each comparison yields one of three outcomes: one algorithm can be "better", "worse", or "equivalent" to another;those algorithms evaluating to have "equivalent" performance are merged into the same performance class. We show that our clustering methodology facilitates algorithm selection with respect to more than one metric;for instance, from the subset of equivalently fast algorithms, one could then select an algorithm that consumes the least energy on a certain device.

关键词： performance analysis algorithm ranking clustering scientific computing distributed computing edge computing

来源：评论

学校读者我要写书评

暂无评论

ProMetaUS: A proactive meta-learning uncertainty-based framework to select models for Dynamic Risk Management

引用

SAFETY SCIENCE 2021年 138卷 105238-105238页

作者： Stefana, Elena Paltrinieri, Nicola Univ Brescia Dept Mech & Ind Engn Via Branze 38 I-25123 Brescia Italy Norwegian Univ Sci & Technol NTNU Fac Engn Dept Mech & Ind Engn SP Andersens Vei 5 N-7491 Trondheim Norway

Safety managers, practitioners, and researchers can employ different models for estimating and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the safety field. The selection of a specific model may depend on the uncertainty associated with its estimation and its impact on the safety-related decision-making process. The recognition of this issue as an example of algorithm Selection Problem (ASP) allows investigating the applicability of meta-learning principles that are scarcely adopted in the risk and safety literature. Consequently, we propose a novel meta-learning inspired framework to proactively rank a set of candidate models for Dynamic Risk Management (DRM) based on desired uncertainty conditions. We denominate this framework ProMetaUS (Proactive Meta-learning and Uncertainty-based Selection for dynamic risk management). To achieve this purpose, our meta-learning system acquires knowledge that relates the characteristics extracted both directly and indirectly from datasets (e.g. data-based, domain-based, simple and fast uncertainty-based, simple and fast sensitivity-based meta-features) to some performance measures of the models. Performance measures include confidence information, shape measurable quantities, safety decision criteria and threshold limits, and sensitivity analysis outputs. We tested the proposed framework in a case study about Oxygen Deficiency Hazard (ODH) assessment by means of @RISK. For each of the five datasets, single-performance measure rankings and a final ranking of the three models are generated. Such rankings are aggregated to obtain the global recommended ranking.

关键词： algorithm ranking Model recommendation Risk assessment Uncertainty quantification Probability Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：