SMART: unique splitting-while-merging framework for gene clustering. [PDF]
PLoS ONE, 2014 Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori.
Rui Fa, David J Roberts, Asoke K Nandi
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A Decision Tree Classification Algorithm Based on Two-Term RS-Entropy [PDF]
EntropyClassification is an important task in the field of machine learning. Decision tree algorithms are a popular choice for handling classification tasks due to their high accuracy, simple algorithmic process, and good interpretability.
Ruoyue Mao, Xiaoyang Shi, Zhiyan Shi
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MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines [PDF]
The Scientific World Journal, 2014 We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property.
Duygu Yilmaz Eroglu, H. Cenk Ozmutlu
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The HSS splitting hierarchical identification algorithms for solving the Sylvester matrix equation
AIMS MathematicsBy combining the hierarchical identification principle with HSS splitting, we presented the HSS splitting hierarchical identification algorithm for solving the Sylvester matrix equation in this paper. To enhance the convergence rate of the algorithm, the
Huiling Wang, Zhaolu Tian, Yufeng Nie
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The unresolved struggle of 16S rRNA amplicon sequencing: a benchmarking analysis of clustering and denoising methods [PDF]
Environmental MicrobiomeBackground Although 16S rRNA gene amplicon sequencing has become an indispensable method for microbiome studies, this analysis is not error-free, and remains prone to several biases and errors.
Mohamed Fares +7 more
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A Generalized Matrix Splitting Algorithm [PDF]
, 2018 Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization, $\ell_1$ norm regularized optimization, and $\ell_0$ norm regularized optimization as special cases.
Ganzhao Yuan +3 more
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Split-Douglas--Rachford Algorithm for Composite Monotone Inclusions and Split-ADMM [PDF]
SIAM Journal on Optimization, 2021 26 ...
Luis M. Bricen͂o-Arias +1 more
openaire +2 more sources
Shear-Wave Splitting Analysis Using Optimization Algorithms
Lithosphere, 2022 Shear-wave splitting (SWS) analysis is used to predict fractures in subsurface media. Specifically, two parameters relevant to SWS analysis (the azimuth of the fast shear wave and the time delay between the fast and slow shear waves) are used to quantify
Zhengtao He, Yuyong Yang, Huailai Zhou
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Land, 2021 Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data ...
Minu Treesa Abraham +4 more
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Application of enhanced benders decomposition algorithm in circular assembly line balancing problem with task splitting. [PDF]
PLoS OneLi P, Ji C.
europepmc +2 more sources