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Discovering predictive ensembles for transfer learning and meta-learning

MACHINE LEARNING 2018年第1期107卷 177-207页

作者： Kordik, Pavel Cerny, Jan Fryda, Tomas FIT CVUT Thakurova 9 Prague 6 Czech Republic

Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this article we show how data-tailored algorithms can be constructed from building blocks on small data sub-samples. Building blocks, typically weak learners, are optimized and evolved into data-tailored hierarchical ensembles. Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set. Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.

关键词： Meta-learning Ensemble learning Evolutionary algorithms Evolutionary programming Combining classifiers Regression models Model blending automatic algorithm selection Map reduce

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FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data

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SCIENTIFIC REPORTS 2024年第1期14卷 1-10页

作者： Elshawi, Radwa Sakr, Sherif Al-Mallah, Mouaz H. Keteyian, Steven J. Brawner, Clinton A. Ehrman, Jonathan K. Univ Tartu Inst Comp Sci Tartu Estonia Houston Methodist DeBakey Heart & Vasc Ctr Houston TX USA Henry Ford Hosp Div Cardiovasc Med 6525 Second Ave Detroit MI 48202 USA

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertensio

关键词： Prediction model Classification techniques Interpretability automatic algorithm selection Hyperparameter optimization

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N ways to simulate short-range particle systems: Automated algorithm selection with the node-level library AutoPas

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COMPUTER PHYSICS COMMUNICATIONS 2022年 273卷 108262-108262页

作者： Gratl, Fabio Alexander Seckler, Steffen Bungartz, Hans-Joachim Neumann, Philipp Tech Univ Munich Chair Sci Comp Comp Sci Munich Germany Helmut Schmidt Univ Chair High Performance Comp Hamburg Germany

AutoPas is an open-source C++ library delivering optimal node-level performance by providing the ideal algorithmic configuration for an arbitrary scenario in a given short-range particle simulation. It acts as a black-box container, internally implementing an extensive set of algorithms, parallelization strategies, and optimizations that are combined dynamically according to the state of the simulation via auto-tuning. This paper gives an overview of the high-level user perspective, as well as the internal view, covering the implemented techniques and features. The library is showcased by incorporating it into the codes LAMMPS and ls1 mardyn, and by investigating various applications. We further outline node-level shared-memory performance and scalability of our auto-tuning software which is comparable to LAMMPS. (C) 2021 Elsevier B.V. All rights reserved.

关键词： HPC N-body simulation Molecular dynamics Auto-tuning automatic algorithm selection Dynamic tuning

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A Hyperheuristic and Reinforcement Learning Guided Meta-heuristic algorithm Recommendation 27

A Hyperheuristic and Reinforcement Learning Guided Meta-heur...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Zhu, Ningning Zhao, Fuqing Cao, Jie Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

ISBN: (纸本)9798350349184;9798350349191

automatic selection of the most appropriate algorithms for complex optimization problems has emerged as a cutting-edge trend in artificial intelligence. This approach circumvents the interpretability challenges posed through trial and error. A hyperheuristic and reinforcement learning-guided meta-heuristic algorithm recommendation (HHRL-MAR) is proposed to facilitate the adaptive selection of a diverse array of meta-heuristic algorithms tailored to the unique characteristics of various problems in this paper. To this end, four meta-heuristics with distinct advantages are integrated to form the action space within the reinforcement learning, serving as the low-level heuristic for hyperheuristic. The incorporated reward mechanism based on the real-time state of the population enhances both the flexibility and accuracy of the algorithm. Three selection strategies in light of simulated annealing and epsilon - greedy are designed to avoid premature convergence associated with a singular selection approach. The experimental results show the efficacy of HHRL-MAR for large-scale complex continuous optimization in terms of accuracy, stability, and convergence speed.

关键词： reinforcement learning hyperheuristic algorithm automatic algorithm selection meta-heuristic algorithm population activation procedure

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MLBCD: a machine learning tool for big clinical data

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2015年第1期3卷 3-3页

作者： Luo, Gang Univ Utah Dept Biomed Informat Salt Lake City UT 84108 USA

Background: Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data,"advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. Methods: This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. Results: The paper describes MLBCD's design in detail. Conclusions: By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

关键词： Machine learning Big clinical data automatic algorithm selection automatic hyper-parameter value selection Entity-Attribute-Value Pivot

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automatic Design of Evolutionary algorithms based on Entropy Triggers

Automatic Design of Evolutionary Algorithms based on Entropy...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： da Silva, Guilherme Ribeiro Basgalupp, Marcio P. Lorena, Ana Carolina Univ Fed Sao Paulo Inst Ciencia & Tecnol Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781509060177

The field of automatic algorithm design has received increasing attention in recent years. From a multitude of available algorithms, a researcher can effectively design a new one customized to his/her own problem. For this, hyper-heuristics techniques have proven to be useful. Their main objective is to search in the space of heuristics rather than in the problem solution space. The present paper proposes a hyper-heuristic for the automatic design of evolutionary algorithms supported by the use of an entropy metric. This metric is used as a trigger mechanism for switching between the algorithms components, aiding the formation of the new hybrid algorithm.

关键词： hyper-heuristics automatic algorithm design grammatical-evolution automatic algorithm selection

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PredicT-ML: a tool for automating machine learning model building with big clinical data

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2016年第1期4卷 5-5页

作者： Luo, Gang Univ Utah Dept Biomed Informat Suite 140421 Wakara Way Salt Lake City UT 84108 USA

Background: Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. Methods: This paper describes our design of and vision for PredicT-ML ( prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. Results: The paper presents the detailed design of PredicT-ML. Conclusions: PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

关键词： Machine learning automatic algorithm selection automatic hyper-parameter value selection Automated temporal aggregation Big clinical data

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Very high-level language design: A viewpoint

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Computer Languages 1975年第1期1卷 3-16页

作者： Tucker, Allen Academic Computation Center Georgetown University Washington DC United States

Recent developments in very high-level language design indicate that these languages hold great promise for improving the level of man-machine communication, and hence improving computer and programmer utilization. (Essentially, a very high-level language one which allows the programmer to specify what to do, rather than how to do it.) This paper surveys these developments, outlines the goals to which an "ideal" very high-level language should aspire, and then presents the design of a very high-level language that would meet these goals. This design is presented in the interest of laying bare some basic design and implementation questions that are inherent to such an achievement. The paper then discusses these questions, indicating both old and new research problems which they suggest. © 1975.

关键词： Ambiguity automatic algorithm selection Command languages Context-sensitivity Data base management systems Extendible languages High-level languages Job control languages Parsing Very high-level languages

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AutoPas: Auto-Tuning for Particle Simulations 33

AutoPas: Auto-Tuning for Particle Simulations

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33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Gratl, Fabio Seckler, Steffen Tchipev, Nikola Bungartz, Hans-Joachim Neumann, Philipp Tech Univ Munich Chair Sci Comp Comp Sci Munich Germany Univ Hamburg Sci Comp Hamburg Germany

ISBN: (纸本)9781538655559

The C++ library AutoPas aims at delivering optimal node-level performance for particle simulations. This paper describes the internally implemented algorithms, and how the library uses auto-tuning to dynamically select their optimal combination at run-time. Results are presented, which show that all available algorithms and configuration options have their specific advantages. To demonstrate the library's capabilities in relevant application settings, it has been integrated into the software package ls1 mardyn. An example of a realistic molecular dynamics simulation from thermodynamics is shown in which AutoPas detects a change in the best possible algorithm configuration. It adapts the simulation algorithm accordingly, sustaining optimal performance without additional user input.

关键词： HPC Molecular Dynamics Auto-Tuning automatic algorithm selection Dynamic Tuning

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