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Comprehensible knowledge model creation for cancer treatment decision making

COMPUTERS IN BIOLOGY AND MEDICINE 2017年 82卷 119-129页

作者： Afzal, Muhammad Hussain, Maqbool Khan, Wajahat Ali Ali, Taqdir Lee, Sungyoung Huh, Eui-Nam Ahmad, Hafiz Farooq Jamshed, Arif Iqbal, Hassan Irfan, Muhammad Hydari, Manzar Abbas Kyung Hee Univ Dept Comp Sci & Engn Seocheon Dong South Korea King Faisal Univ CCSIT Alahsa Saudi Arabia Shaukat Khanum Mem Canc Hosp & Res Ctr Lahore Pakistan Ohio State Univ Dept Otolaryngol & Head & Neck Surg Columbus OH 43210 USA

Background: A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. Materials and Methods: An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality redaction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services" clinical decisions and education support. Results: Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Conclusion: Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CICM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education.

关键词： Knowledge acquisition algorithm selection Prediction model Decision support Education support

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Generating New Space-Filling Test Instances for Continuous Black-Box Optimization

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EVOLUTIONARY COMPUTATION 2020年第3期28卷 379-404页

作者： Munoz, Mario A. Smith-Miles, Kate Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

This article presents a method to generate diverse and challenging new test instances for continuous black-box optimization. Each instance is represented as a feature vector of exploratory landscape analysis measures. By projecting the features into a two-dimensional instance space, the location of existing test instances can be visualized, and their similarities and differences revealed. New instances are generated through genetic programming which evolves functions with controllable characteristics. Convergence to selected target points in the instance space is used to drive the evolutionary process, such that the new instances span the entire space more comprehensively. We demonstrate the method by generating two-dimensional functions to visualize its success, and ten-dimensional functions to test its scalability. We show that the method can recreate existing test functions when target points are co-located with existing functions, and can generate new functions with entirely different characteristics when target points are located in empty regions of the instance space. Moreover, we test the effectiveness of three state-of-the-art algorithms on the new set of instances. The results demonstrate that the new set is not only more diverse than a well-known benchmark set, but also more challenging for the tested algorithms. Hence, the method opens up a new avenue for developing test instances with controllable characteristics, necessary to expose the strengths and weaknesses of algorithms, and drive algorithm development.

关键词： algorithm selection benchmarking black-box continuous optimization exploratory landscape analysis instance generator

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Multi-armed bandits with censored consumption of resources

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MACHINE LEARNING 2023年第1期112卷 217-240页

作者： Bengs, Viktor Huellermeier, Eyke Ludwig Maximilians Univ Munchen Inst Informat Munich Germany Munich Ctr Machine Learning Munich Germany

We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates both the actual amount of consumed resources of each learning round and the optimality of realizable rewards as well as the risk of exceeding the allocated resource limit. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We propose a UCB-inspired online learning algorithm, which we analyze theoretically in terms of its regret upper bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms.

关键词： algorithm selection Bivariate feedback Censored feedback Exploration Exploitation

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Selecting appropriate machine learning methods for digital soil mapping

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APPLIED MATHEMATICAL MODELLING 2020年第0期81卷 401-418页

作者： Khaledian, Yones Miller, Bradley A. Iowa State Univ Dept Agron Ames IA 50011 USA

Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to identify relationships between soil properties and multiple covariates that can be detected across landscapes. Selecting the appropriate algorithm for model building is critical for optimizing results in the context of the available data. Over the past decade, many studies have tested different machine learning (ML) approaches on a variety of soil data sets. Here, we review the application of some of the most popular ML algorithms for digital soil mapping. Specifically, we compare the strengths and weaknesses of multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), Cubist, random forest (RF), and artificial neural networks (ANN) for DSM. These algorithms were compared on the basis of five factors: (1) quantity of hyperparameters, (2) sample size, (3) covariate selection, (4) learning time, and (5) interpretability of the resulting model. If training time is a limitation, then algorithms that have fewer model parameters and hyperparameters should be considered, e.g., MLR, KNN, SVR, and Cubist. If the data set is large (thousands of samples) and computation time is not an issue, ANN would likely produce the best results. If the data set is small (<100), then Cubist, KNN, RF, and SVR are likely to perform better than ANN and MLR. The uncertainty in predictions produced by Cubist, KNN, RF, and SVR may not decrease with large datasets. When interpretability of the resulting model is important to the user, Cubist, MLR, and RF are more appropriate algorithms as they do not function as "black boxes." There is no one correct approach to produce models for predicting the spatial distribution of soil properties. Nonetheless, some algorithms are more appropriate than others considering the nature of the data and purpose of mapping activity. (C) 2019 Elsevier Inc. All rights reserved.

关键词： algorithm selection Digital soil mapping Machine learning Model evaluation

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An Instance Space Analysis of Constrained Multiobjective Optimization Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1427-1439页

作者： Alsouly, Hanan Kirley, Michael Munoz, Mario Andres Univ Melbourne Sch Comp & Informat Melbourne Vic 3010 Australia ARC Ctr Optimisat Technol Integrated Methodol & A Melbourne Vic 3010 Australia Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia

Constrained multiobjective optimization problems (CMOPs) are generally more challenging than unconstrained problems. This in part can be attributed to the infeasible region generated by the constraint functions, the interaction between constraints and objectives, or both. In this article, we explore the relationship between the performance of constrained multiobjective evolutionary algorithms (CMOEAs) and the instance characteristics of CMOP using instance space analysis (ISA). To do this, we extend recent work on Landscape Analysis features for characterizing CMOPs. Specifically, we introduce new features to describe the multiobjective-violation landscape, formed by the interaction between constraint violation and multiobjective fitness. The detailed evaluation of the algorithm footprints, spanning eight CMOP benchmark suites and 15 CMOEAs, demonstrates that ISA effectively captures the strength and weakness of the CMOEAs. We conclude that two characteristics, the isolation of nondominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems lack of diversity to represent the real-world problems and to fully reveal the efficacy of CMOEAs evaluated.

关键词： algorithm selection constrained multiobjective optimization evolutionary algorithm landscape analysis problem characterization

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Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio

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MACHINE LEARNING 2022年第12期111卷 4565-4584页

作者： Konig, Matthias Hoos, Holger H. van Rijn, Jan N. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Univ British Columbia Vancouver BC Canada

Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines, MIPVerify and Venus, and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively.

关键词： Neural network verification Mixed integer programming Automated algorithm configuration algorithm selection

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A recommendation system for meta-modeling: A meta-learning based approach

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 46卷 33-44页

作者： Cui, Can Hu, Mengqi Weir, Jeffery D. Wu, Teresa Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Air Force Inst Technol Dept Operat Sci Wright Patterson AFB OH 45433 USA

Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based and model-based learning approaches for optimal framework design, and (4) performance evaluation module which introduces two criteria, Spearman's ranking correlation coefficient and hit ratio, to evaluate the system on the accuracy of model ranking prediction and the precision of the best model recommendation, respectively. To further improve the performance of meta-learning for meta-modeling recommendation, different types of feature reduction techniques, including singular value decomposition, stepwise regression and ReliefF, are studied. Experiments show that our proposed framework is able to achieve 94% correlation on model rankings, and a 91% hit ratio on best model recommendation. Moreover, the computational cost of meta-modeling recommendation is significantly reduced from an order of minutes to seconds compared to traditional trial-and-error and ensemble process. The proposed framework can significantly advance the research in meta-modeling recommendation, and can be applied for data-driven system modeling. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Meta-learning Meta-model Simulation Recommendation system algorithm selection Feature reduction

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Taking the human out of decomposition-based optimization via artificial intelligence, Part I: Learning when to decompose

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COMPUTERS & CHEMICAL ENGINEERING 2024年 186卷

作者： Mitrai, Ilias Daoutidis, Prodromos Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN 55455 USA

In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition -based solution method. In this approach, an optimization problem is represented as a graph that captures the structural and functional coupling among the variables and constraints of the problem via an appropriate set of features. Given this representation, a graph classifier can be built to assist a solver in selecting the best solution strategy for a given problem with respect to some metric of choice. The proposed approach is used to develop a classifier that determines whether a convex Mixed Integer Nonlinear Programming problem should be solved using branch and bound or the outer approximation algorithm. Finally, it is shown how the learned classifier can be incorporated into existing mixed integer optimization solvers.

关键词： algorithm selection Decomposition-based solution algorithms Deep learning Graph neural networks Convex MINLP Graph classification

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Sparkle: Toward Accessible Meta-algorithmics for Improving the State of the Art in Solving Challenging Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第6期26卷 1351-1364页

作者： Van der Blom, Koen Hoos, Holger H. H. Luo, Chuan Rook, Jeroen G. G. Leiden Univ Fac Sci NL-2311 EZ Leiden Netherlands Sorbonne Univ LIP6 CNRS F-75005 Paris France Rhein Westfal TH Aachen Dept Comp Sci D-52062 Aachen Germany Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada Beihang Univ Sch Software Beijing 100190 Peoples R China Univ Twente Data Management & Biometr DMB NL-7522 NB Enschede Netherlands

Many fields of computational science advance through improvements in the algorithms used for solving key problems. These advancements are often facilitated by benchmarks and competitions that enable performance comparisons and rankings of solvers. Simultaneously, meta-algorithmic techniques, such as automated algorithm selection and configuration, enable performance improvements by utilizing the complementary strengths of different algorithms or configurable algorithm components. In fact, meta-algorithms have become major drivers in advancing the state of the art in solving many prominent computational problems. However, meta-algorithmic techniques are complex and difficult to use correctly, while their incorrect use may reduce their efficiency, or in extreme cases, even lead to performance losses. Here, we introduce the Sparkle platform, which aims to make meta-algorithmic techniques more accessible to nonexpert users, and to make these techniques more broadly available in the context of competitions, to further enable the assessment and advancement of the true state of the art in solving challenging computational problems. To achieve this, Sparkle implements standard protocols for algorithm selection and configuration that support easy and correct use of these techniques. Following an experiment, Sparkle generates a report containing results, problem instances, algorithms, and other relevant information, for convenient use in scientific publications.

关键词： algorithm configuration algorithm selection benchmarking competitions meta-algorithms software tools

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Efficient block scheduling to minimize context switching time for programmable embedded processors

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DESIGN AUTOMATION FOR EMBEDDED SYSTEMS 1999年第4期4卷 311-327页

作者： Hong, IK Potkonjak, M Papaefthymiou, M Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Univ Michigan Dept EECS Ann Arbor MI 48109 USA

Scheduling is one of the most often addressed optimization problems in DSP compilation, behavioral synthesis, and system-level synthesis research. With the rapid pace of changes in modern DSP applications requirements and implementation technologies, however, new types of scheduling challenges arise. This paper is concerned with the problem of scheduling blocks of computations in order to optimize the efficiency of their execution on programmable embedded systems under a realistic timing model of their processors. We describe an effective scheme for scheduling the blocks of any computation on a given system architecture and with a specified algorithm implementing each block. We also present algorithmic techniques for performing optimal block scheduling simultaneously with optimal architecture and algorithm selection. Our techniques address the block scheduling problem for both single- and multiple-processor system platforms and for a variety of optimization objectives including throughput, cost, and power dissipation. We demonstrate the practical effectiveness of our techniques on numerous designs and synthetic examples.

关键词： block scheduling context switching minimization architecture selection algorithm selection

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