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16th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA)

作者： Donisi, Leandro Moretta, Pasquale Coccia, Armando Amitrano, Federica Biancardi, Arcangelo D'Addio, Giovanni Univ Naples Federico II Dept Adv Biomed Sci Naples Italy ICS Maugeri Telese Terme Italy ICS Maugeri IRCCS Dept Neurorehabil Telese Terme Italy Univ Naples Federico II Dept ITEE Naples Italy ICS Maugeri IRCCS Dept Bioengn Telese Terme Italy

ISBN: (纸本)9781665419147

Unilateral Spatial Neglect is a cognitive impairment of neuropsychological interest that is a consequence of stroke able to influence negatively the rehabilitation outcome of patients with stroke. The aim of the study is to explore the feasibility of machine learning to classify stroke patients with and without unilateral spatial neglect using clinical features. We performed the study using a machine learning approach by means the following tree-based algorithms: Decision tree, Random Forest, Rotation Forest, AdaBoost of decision stumps and Gradient Boost tree using six clinical features both numerical and nominal: Montreal Cognitive Assessment, Functional Independence Measure scale, Barthel Index, aetiology, site of brain lesion and presence of hemiparesis at lower limbs. tree-based Machine learning analysis achieved interesting results in terms of evaluation metrics scores;the best algorithm was Random Forest with an Accuracy, Sensitivity, Specificity, Precision and Area under the Receiver Operating Characteristic curve equal to 0.92, 0.83, 1.00, 1.00, 0.95 respectively. The study demonstrated the proposed combination of clinical features and algorithms represents a valuable approach to automatically classify stroke patients with and without Unilateral Spatial Neglect. The future investigations on enriched datasets will further confirm the potential application of this methodology as prognostic support to be chosen among those already implemented in the clinical field.

关键词： Machine Learning Rehabilitation Stroke tree-based algorithms Unilateral Spatial Neglect

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Efficient machine learning models for prediction of concrete strengths

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CONSTRUCTION AND BUILDING MATERIALS 2021年第PartB期266卷 120950-120950页

作者： Hoang Nguyen Thanh Vu Vo, Thuc P. Huu-Tai Thai Northumbria Univ Dept Mech & Construct Engn Newcastle Upon Tyne NE1 8ST Tyne & Wear England Oracle Digital Assistant Oracle Australia La Trobe Univ Sch Engn & Math Sci Bundoora Vic 3086 Australia Univ Melbourne Dept Infrastruct Engn Parkville Vic 3010 Australia

In this study, an efficient implementation of machine learning models to predict compressive and tensile strengths of high-performance concrete (HPC) is presented. Four predictive algorithms including support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost) are employed. The process of hyperparameter tuning is based on random search that results in trained models with better predictive performances. In addition, the missing data is handled by filling with the mean of the available data which allows more information to be used in the training process. The results on two popular datasets of compressive and tensile strengths of high performance concrete show significant improvement of the current approach in terms of both prediction accuracy and computational effort. The comparative studies reveal that, for this particular prediction problem, the trained models based on GBR and XGBoost perform better than those of SVR and MLP. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： High performance concrete Ensemble learning Support vector machine Multi-layer Perceptron tree-based algorithms

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Portal: A High-Performance Language and Compiler for Parallel N-body Problems 33

Portal: A High-Performance Language and Compiler for Paralle...

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

作者： Beni, Laleh Aghababaie Ramanan, Saikiran Chandramowlishwaran, Aparna Univ Calif Irvine Irvine CA 92717 USA

ISBN: (纸本)9781728112466

There is a big gap between the algorithm one designs on paper and the code that runs efficiently on a parallel system. Our goal is to combine the body of work in compilers, performance optimization, and the domain of N-body problems to build a system where domain scientists can write programs at a high level while attaining performance of code written by experts at the low level. This paper presents Portal, a domain-specific language and compiler designed to enable high-performance implementations of N-body problems on modern multicore systems. Our goal in the development of Portal is three-fold, (a) to implement scalable, fast algorithms that have O (n log n) and O (n) complexity, (b) to design an intuitive language to enable rapid implementations of a variety of problems, and (c) to enable parallel large-scale problems to run on multicore systems. We target N-body problems in various domains from machine learning to scientific computing that can be expressed in Portal to obtain an out-of-the-box optimized parallel implementation. Experimental results on 6 N-body problems show that Portal is within a factor of 5% on average of expert hand-optimized C++ code on a dual-socket AMD EPYC processor. To our knowledge, there are no known libraries or frameworks that implement parallel asymptotically optimal algorithms for the class of generalized N-body problems and Portal aims to fill this gap. Moreover, the Portal language and intermediate algorithm representation are portable and easily extensible to different platforms.

关键词： Domain-specific language LLVM N-body problems tree-based algorithms

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On tree-based Methods for Similarity Learning 5th

On Tree-Based Methods for Similarity Learning

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5th International Conference on Machine Learning, Optimization, and Data Science (LOD)

作者： Clemencon, Stephan Vogel, Robin Inst Polytech Paris LTCI Telecom Paris Paris France IDEMIA Courbevoie France

ISBN: (纸本)9783030375997;9783030375980

In many situations, the choice of an adequate similarity measure or metric on the feature space dramatically determines the performance of machine learning methods. Building automatically such measures is the specific purpose of metric/similarity learning. In [21], similarity learning is formulated as a pairwise bipartite ranking problem: ideally, the larger the probability that two observations in the feature space belong to the same class (or share the same label), the higher the similarity measure between them. From this perspective, the ROC curve is an appropriate performance criterion and it is the goal of this article to extend recursive tree-based ROC optimization techniques in order to propose efficient similarity learning algorithms. The validity of such iterative partitioning procedures in the pairwise setting is established by means of results pertaining to the theory of U-processes and from a practical angle, it is discussed at length how to implement them by means of splitting rules specifically tailored to the similarity learning task. Beyond these theoretical/methodological contributions, numerical experiments are displayed and provide strong empirical evidence of the performance of the algorithmic approaches we propose.

关键词： Metric-learning Rate bound analysis Similarity learning tree-based algorithms U-processes

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Analyzing of incremental high utility pattern mining based on tree structures

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2017年第1期7卷 1页

作者： Lee, Judae Yun, Unil Lee, Gangin Sejong Univ Dept Comp Engn Seoul South Korea

Since the concept of high utility pattern mining was proposed to solve the drawbacks of traditional frequent pattern mining approach that cannot handle various features of real-world applications, many different techniques and algorithms for high utility pattern mining have been developed. Moreover, several advanced methods for incremental data processing have been proposed in recent years as the sizes of recent databases obtained in the real world become larger. In this paper, we introduce the basic concept of incremental high utility pattern mining and analyze various relevant methods. In addition, we also conduct performance evaluation for the methods with famous benchmark datasets in order to determine their detailed characteristics. The evaluation shows that the less candidate patterns make algorithms faster.

关键词： Data mining Pattern mining Utility mining Incremental mining High utility patterns tree-based algorithms

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The expected bit complexity of the von Neumann rejection algorithm

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STATISTICS AND COMPUTING 2017年第3期27卷 699-710页

作者： Devroye, Luc Gravel, Claude McGill Univ Sch Comp Sci Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada

In 1952, von Neumann introduced the rejection method for random variate generation. We revisit this algorithm when we have a source of perfect bits at our disposal. In this random bit model, there are universal lower bounds for generating a random variate with a given density to within an accuracy derived by Knuth and Yao, and refined by the authors. In general, von Neumann's method fails in this model. We propose a modification that insures proper behavior for all Riemann-integrable densities on compact sets, and show that the expected number of random bits needed behaves optimally with respect to universal lower bounds. In particular, we introduce the notion of an oracle that evaluates the supremum and infimum of a function on any rectangle of , and develop a quadtree-style extension of the classical rejection method.

关键词： Random number generation Random bit model Von Neumann sampling algorithm tree-based algorithms Random sampling Entropy

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Applying Classification Techniques To Predict Students' Academic Results

Applying Classification Techniques To Predict Students' Acad...

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IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)

作者： Senthil, S. Lin, Wong Mu Reva Univ Sch Comp Sci & Applicat Bengaluru India

ISBN: (纸本)9781509049974

The ability to predict students' academic results is due to the application of various models developed using Data Mining techniques. It is one of the many applications available in Educational Data Mining (EDM). Although there are many classification algorithms available and there have been many researches done to compare certain algorithms, this study aims to compare Bayes-based, ANN-based, Regression-based, SVM-based, Instance-based, tree-based and Rule-based classification algorithms using a dataset downloaded online from UCI Machine Learning Repository. The data were preprocessed and categorized to different datasets before applying Data Mining techniques. Dimension reduction and data re-balancing were applied and proved to increase prediction accuracy. The algorithms were compared in terms of accuracy rate, precision rate, AUC and model building time. tree-based algorithms performed better than other categories of algorithms. Within tree-based algorithms, RandomForest algorithm proved to be the best classification algorithm. tree-based and Rule-based algorithms reveal the key attributes contributing to accurate prediction of students' results.

关键词： Educational Data Mining (EDM) Classification Knowledge Discovery in Databases (KDD) tree-based algorithms Rule-based algorithms

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A tree-based algorithm for multicasting in 2D torus networks

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EGYPTIAN INFORMATICS JOURNAL 2015年第1期16卷 45-53页

作者： Abd El-Baky, M. A. Fayoum Univ Fac Sci Dept Math Faiyum Egypt

Torus network has become increasingly important to multicomputer design because of its many desirable properties including low bandwidth and fixed degree of nodes. Also, torus networks can be partitioned into mesh networks. The multicast pattern, in which one source node sends the same message to multiple destination nodes, is the essential pattern in a wide variety of applications. This paper proposes a multicast routing scheme in 2D torus networks. The proposed scheme is a tree-based Algorithm which Splits torus Networks into two Equally Meshes, hence it is called TASNEM. TASNEM algorithm is a tree-based technique, in which the router simultaneously sends incoming flits on more than one outgoing channel. It requires at most two start-up times, one for each mesh subnetwork. For each mesh subnetwork, the source node delivers a message to the destination nodes along one main path and different horizontal paths branched from the main path. TASNEM algorithm can achieve high degree of parallelism and low communication latency over a wide range of traffic loads. Performance results of a simulation study on torus networks are discussed to compare TASNEM algorithm with some previous algorithms. (C) 2015 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license

关键词： Multicast routing Torus network tree-based algorithms Communication latency

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CLASSIFICATION algorithms USING ADAPTIVE PARTITIONING

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ANNALS OF STATISTICS 2014年第6期42卷 2141-2163页

作者： Binev, Peter Cohen, Albert Dahmen, Wolfgang DeVore, Ronald Univ S Carolina Dept Math Columbia SC 29208 USA Univ Paris 06 UMR 7598 Lab Jacques Louis Lions F-75005 Paris France Rhein Westfal TH Aachen Inst Geometrie & Prakt Math D-52056 Aachen Germany Texas A&M Univ Dept Math College Stn TX 77840 USA

algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be-viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335-1353;Mach. Learn. 66 (2007) 209-242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter alpha of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness beta of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.

关键词： Binary classification adaptive methods set estimators tree-based algorithms

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Principal Curve algorithms for Partitioning High-Dimensional Data Spaces

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第3期22卷 367-380页

作者： Zhang, Junping Wang, Xiaodan Kruger, Uwe Wang, Fei-Yue Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Petr Inst Dept Chem Engn Abu Dhabi 2533 U Arab Emirates Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Arizona Tucson AZ 85719 USA

Most partitioning algorithms iteratively partition a space into cells that contain underlying linear or nonlinear structures using linear partitioning strategies. The compactness of each cell depends on how well the (locally) linear partitioning strategy approximates the intrinsic structure. To partition a compact structure for complex data in a nonlinear context, this paper proposes a nonlinear partition strategy. This is a principal curve tree (PC-tree), which is implemented iteratively. Given that a PC passes through the middle of the data distribution, it allows for partitioning based on the arc length of the PC. To enhance the partitioning of a given space, a residual version of the PC-tree algorithm is developed, denoted here as the principal component analysis tree (PCR-tree) algorithm. Because of its residual property, the PCR-tree can yield the intrinsic dimension of high-dimensional data. Comparisons presented in this paper confirm that the proposed PC-tree and PCR-tree approaches show a better performance than several other competing partitioning algorithms in terms of vector quantization error and nearest neighbor search. The comparison also shows that the proposed algorithms outperform competing linear methods in total average coverage which measures the nonlinear compactness of partitioning algorithms.

关键词： Manifold learning principal component analysis principal curves space partitioning tree-based algorithms

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