An Efficient CGM-Based Parallel Algorithm for Solving the Optimal Binary Search Tree Problem Through One-to-All Shortest Paths in a Dynamic Graph

Tchendji, Vianney Kengne; Zeutouo, Jerry Lacmou

doi:10.1007/s41019-019-0093-9

Cited by 10 publications

(1 citation statement)

References 25 publications

(33 reference statements)

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“…Also, when we train the logistic regression classifiers, the gradient of each position of each shapelet (line 19 in Algorithm 3) also needs to be calculated by the for loop. In ELIS++, we accelerate these computations by parallel computation [26]. We utilize the ubiquitous Single Instruction Multiple Data (SIMD) architecture, which assigns multiple threads to process the same set of instructions on multiple data.…”

Section: Accelerate the Learning By Parallel Computationmentioning

confidence: 99%

ELIS++: a shapelet learning approach for accurate and efficient time series classification

et al. 2021

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In recent years, time series classification with shapelets, due to the high accuracy and good interpretability, has attracted considerable interests. These approaches extract or learn shapelets from the training time series. Although they can achieve higher accuracy than other approaches, there still confront some challenges. First, they may suffer from low accuracy in the case of small training dataset. Second, they must manually set some parameters, like the number of shapelets and the length of each shapelet beforehand, and some hyper-parameters, like learning rate and regulation weight, which are difficult to set without prior knowledge. Third, extracting or learning shapelets incurs a huge computation cost, due to the huge search space. In this paper, we extend our previous shapelet learning approach ELIS to ELIS++. To improve the accuracy on the small training dataset, we propose a data augmentation approach. To learn the higher quality shapelets, based on the PAA shapelet candidates search technique proposed in ELIS, ELIS++ first propose a novel entropy-based approach shapelet candidate selection mechanism to discover shapelet candidates, and then applies the logistic regression model to adjust shapelets.To avoid setting other parameters manually, we propose a Bayesian Optimization based approach. Moreover, two techniques are proposed to improve the efficiency, coarse-grained shapelet adjustment and SIMD-based parallel computation. We conduct extensive experiments on 35 UCR datasets, and results verify the effectiveness and efficiency of ELIS++.

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Section: Accelerate the Learning By Parallel Computationmentioning

confidence: 99%

ELIS++: a shapelet learning approach for accurate and efficient time series classification

et al. 2021

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High-performance CGM-based parallel algorithms for minimum cost parenthesizing problem

2021

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Dynamic scheduling method for data relay satellite networks considering hybrid system disturbances

Li,

Chen,

Luo

et al. 2023

Complex Intell. Syst.

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System disturbances, such as the change of required service durations, the failure of resources, and temporary tasks during the scheduling process of data relay satellite network (DRSN), are difficult to be predicted, which may lead to unsuccessful scheduling of tasks. A high-efficiency and robust DRSN calls for smarter and more flexible disturbances elimination strategies. Here, we unify the above three system disturbances as temporary task arrival and extend the static scheduling model of DRSN. Specifically, we derive and define a scheduling model that unifies the static scheduling and dynamic scheduling processes. Meanwhile, we propose a k-step dynamic scheduling algorithm considering breakpoint transmission (k-steps-BT) to solve the above model. Based on the principle of backtracking algorithm and search tree, k-steps-BT can eliminate disturbances quickly by rescheduling tasks and can determine the rescheduling scheme when temporary tasks arrive. Finally, extensive experiments are carried out to verify the proposed model and algorithm. The results show that the proposed model and algorithm can significantly improve the task completion rate of dynamic scheduling without drastic adjustments to the static scheduling scheme.

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An Efficient CGM-Based Parallel Algorithm for Solving the Optimal Binary Search Tree Problem Through One-to-All Shortest Paths in a Dynamic Graph

Cited by 10 publications

References 25 publications

ELIS++: a shapelet learning approach for accurate and efficient time series classification

ELIS++: a shapelet learning approach for accurate and efficient time series classification

High-performance CGM-based parallel algorithms for minimum cost parenthesizing problem

Dynamic scheduling method for data relay satellite networks considering hybrid system disturbances

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