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About 10,000 papers for “Selection algorithm”

The selection algorithm flowchart.

Shiyu Wang (813972), Jinglun Lai (22439303), Jiahao Chen (194408) et al. · Figshare · 2025

<div> Background The renal safety profile of sacubitril/valsartan remains debated in clinical practice. To address this uncertainty, we conducted a pharmacovigilance analysis of acute renal adverse events associated with sacubitril/valsartan using the FDA Adverse Event Reporting System (FAERS) database. Methods Using data from the FAERS database, we conducted an observational pharmacovigilance study from the first quarter (Q1) of 2015 to the fourth quarter of 2024 (Q4). The disproportionality analysis was performed using four algorithms: the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS). Results A total of 16,144,939 adverse events (AEs) were available on FAERS database, among these, 132,255 cases were associated with sacubitril/valsartan, 2,205 cases related to narrow acute renal failure (SMQs) and 3,232 cases with broad SMQs. And sacubitril/valsartan and acute renal adverse showed significant association both in narrow SMQs (ROR = 2.37; PRR = 2.34, χ2 = 1703.73; EBGM05 = 2.26) and the broad SMQs (ROR = 2.55, PRR = 2.51, χ2 = 2941.80, EBGM05 = 2.43). A total of 10 (19.23% of total) positive signals were obtained using the above four algorithms. Median age was 71 years, time to onset was within 30 days, and hospitalization rates of acute renal failure outcomes were 14.33% (narrow SMQs) and 30.69% (broad SMQs). Conclusion The widespread use of sacubitril/valsartan has raised safety concerns, particularly regarding acute renal failure. Our study, leveraging real-world FAERS data, systematically analyzed this association, providing new evidence on acute renal failure risks. </div>

Cited by 0Open access

robsel: Robust Selection Algorithm

Chau Tran, Sang‐Yun Oh, Pedro Cisneros‐Velarde et al. · 2021

An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) &lt;<a href="http://proceedings.mlr.press/v108/cisneros20a.html" target="_top">http://proceedings.mlr.press/v108/cisneros20a.html</a>&gt;.

Cited by 0Open access

Algorithm for cases selection.

Dieudonné Nkoghe (184002), Roland Fabrice Kassa (184006), Mélanie Caron (184012) et al. · Figshare · 2015

Cited by 0Open access

Algorithm selection for SMT

Joseph Scott, Aina Niemetz, Mathias Preiner et al. · International Journal on Software Tools for Technology Transfer · 2023

Cited by 8

Online metaheuristic algorithm selection

Kazem Meidani, Seyedali Mirjalili, Amir Barati Farimani · Expert Systems with Applications · 2022

The performance of optimization algorithms significantly depends on the landscape of the problems. It is known that there is no single algorithm that outperforms others on problems with different fitness landscapes. One of the issues in metaheuristic algorithms is keeping the balance between exploration and exploitation. The features extracted from analysis of fitness landscapes can be used to select the suitable algorithm for the given problem. However, these features are usually expensive and extracted prior to the optimization process which leads to a single algorithm to be selected. In this work, we propose an intelligent switch mechanism that enjoys an efficient non-convex ratio (ENCR) feature extracted online during the optimization to switch between two choices of algorithms, each favoring a type of landscape in terms of modality. For this work, two case studies including a pair of Harris hawks optimizer (HHO) and differential evolution (DE) and another pair of multiverse optimizer (MVO) and moth-flame optimizer (MFO) are selected among several algorithms to evaluate the performance of this framework. The proposed one-way and two-way switch algorithms take advantage of the merits of the two base algorithms to reach better final solutions and higher convergence rates in the majority of case studies. The overall comparison and ranking of the algorithms, including a random switch baseline, demonstrates the superiority of the intelligent switch mechanisms over the baselines.

Cited by 18Open access

Algorithm for patient selection.

Jun Wang (5906), Elizabeth A. Mittendorf (527360), Aysegul A. Sahin (264704) et al. · Figshare · 2015

&lt;p&gt;The Surveillance Epidemiology and End Results 1998–2009 database was used to identify patients diagnosed with invasive lobular carcinoma (ILC). Patients were excluded if their disease stage was unknown, if they had stage III or stage IV disease, if their follow-up time was less than 24 months, if they did not undergo surgery, underwent mastectomy or did not receive radiation as a component of breast conserving therapy. Patients who underwent breast conserving therapy (BCT) who had more than 2 positive lymph nodes were also excluded. This left a final study cohort of 1,269 patients with T1–T2 ILC with 1–2 positive lymph nodes who underwent BCT.&lt;/p&gt;

Cited by 0Open access

Recommendations for algorithm selection.

Markus Goldstein (2616778), Seiichi Uchida (470479) · Figshare · 2016

&lt;p&gt;Qualitatively judgments are given from very bad (− −) over average (o) to very good (++).&lt;/p&gt;

Cited by 1Open access

Algorithm for site selection.

Katie M. Clow (4720602), Patrick A. Leighton (507814), Nicholas H. Ogden (459842) et al. · Figshare · 2017

&lt;p&gt;Sites were selected from the 154 sites visited in 2014 or 2015 [&lt;a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189393#pone.0189393.ref008" target="_blank"&gt;8&lt;/a&gt;,&lt;a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189393#pone.0189393.ref017" target="_blank"&gt;17&lt;/a&gt;]. Sites were classified based on &lt;i&gt;I&lt;/i&gt;. &lt;i&gt;scapularis&lt;/i&gt; status. If &lt;i&gt;I&lt;/i&gt;. &lt;i&gt;scapularis&lt;/i&gt; had not been previously detected at the site, the sites were further classified based on the results of the spatial scan statistics and creation of buffers. Sites within this category were selected to explore the range expansion of &lt;i&gt;I&lt;/i&gt;. &lt;i&gt;scapularis&lt;/i&gt;. If &lt;i&gt;I&lt;/i&gt;. &lt;i&gt;scapularis&lt;/i&gt; had previously been detected at the site, the site was further classified by &lt;i&gt;B&lt;/i&gt;. &lt;i&gt;burgdorferi&lt;/i&gt; status. Sites within this category were selected to explore the emergence of &lt;i&gt;B&lt;/i&gt;. &lt;i&gt;burgdorferi&lt;/i&gt;.&lt;/p&gt;

Cited by 0Open access

Algorithm of patient selection.

Shunji Hirose (3403325), Koshi Matsumoto (550754), Masayuki Tatemichi (5340128) et al. · Figshare · 2020

&lt;p&gt;NAFLD, nonalcoholic fatty liver disease; NAFL, nonalcoholic fatty liver; NASH, nonalcoholic steatohepatitis.&lt;/p&gt;

Cited by 0Open access

Verification Based Algorithm Selection

Martin Lukáč, Michitaka Kameyama · 2023

Algorithm selection is a general purpose optimization based on meta-learning. It uses meta-features to select algorithms on a case by case basis for the most accurate result. In real world problems such as image understanding or image segmentation the meta-features are difficult to obtain due to high diversity and lack of structure in the input data. Therefore algorithm selection in real world problems has been traditionally using image features that are also used for the task processing. Such features however might are not well suited for algorithm selection due to their task specific purpose. In order to solve this problem we propose an improved verification based approach to algorithm selection. First, we build a verification table indicating each algorithms ability to generate content that would satisfy verification for each verification attribute. Using these statistics as basis, we create a multi-algorithm verification map (MAVM). This map is built as a statistical co-occurrences of individual algorithm's ability to satisfy relational verification process of different attributes. The MAVM is reformatted so that for each relational attribute we obtain a co-occurrence of success of each algorithm. The resulting table then used for selection based on the verification result given an algorithm. The advantages of the proposed method are lesser computational cost, possibility to determine ideal combination of algorithms on a case by case basis and provides a basis for algorithm design.

Cited by 0

Reinforcement Learning Algorithm Selection

Romain Laroche, Raphaël Féraud · arXiv (Cornell University) · 2017

This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning. The setup is as follows: given an episodic task and a finite number of off-policy RL algorithms, a meta-algorithm has to decide which RL algorithm is in control during the next episode so as to maximize the expected return. The article presents a novel meta-algorithm, called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is to freeze the policy updates at each epoch, and to leave a rebooted stochastic bandit in charge of the algorithm selection. Under some assumptions, a thorough theoretical analysis demonstrates its near-optimality considering the structural sampling budget limitations. ESBAS is first empirically evaluated on a dialogue task where it is shown to outperform each individual algorithm in most configurations. ESBAS is then adapted to a true online setting where algorithms update their policies after each transition, which we call SSBAS. SSBAS is evaluated on a fruit collection task where it is shown to adapt the stepsize parameter more efficiently than the classical hyperbolic decay, and on an Atari game, where it improves the performance by a wide margin.

Cited by 3Open access

Algorithm Selection via Ranking

Richard J. Oentaryo, Stephanus Daniel Handoko, Hoong Chuin Lau · Proceedings of the AAAI Conference on Artificial Intelligence · 2015

The abundance of algorithms developed to solve different problems has given rise to an important research question: How do we choose the best algorithm for a given problem? Known as algorithm selection, this issue has been prevailing in many domains, as no single algorithm can perform best on all problem instances. Traditional algorithm selection and portfolio construction methods typically treat the problem as a classification or regression task. In this paper, we present a new approach that provides a more natural treatment of algorithm selection and portfolio construction as a ranking task. Accordingly, we develop a Ranking-Based Algorithm Selection (RAS) method, which employs a simple polynomial model to capture the ranking of different solvers for different problem instances. We devise an efficient iterative algorithm that can gracefully optimize the polynomial coefficients by minimizing a ranking loss function, which is derived from a sound probabilistic formulation of the ranking problem. Experiments on the SAT 2012 competition dataset show that our approach yields competitive performance to that of more sophisticated algorithm selection methods.

Cited by 13Open access

VIF auto-selection algorithm.

Xiaomeng Zhang (182897), Mark D. Pagel (527374), Amanda F. Baker (527375) et al. · Figshare · 2015

&lt;p&gt;VIFs are selected by comparing the signal change between pre- and post- injection (a); VIFs were further restrained (dot line) by comparing their slopes (b); The final VIF was the average of VIFs with similar slopes (thick red line).&lt;/p&gt;

Cited by 0Open access

Algorithm of patient selection.

Koichi Sugimoto (582166), Akiomi Yoshihisa (582157), Kazuhiko Nakazato (582168) et al. · Figshare · 2016

&lt;p&gt;RHC, right heart catheterization; PAP, pulmonary arterial pressure; Pcw, pulmonary capillary wedge pressure; IPAH, idiopathic pulmonary hypertension; CTD-PH, connective tissue disease-associated pulmonary hypertension; CTEPH, chronic thromboembolic pulmonary hypertension; LHD-PH, pulmonary hypertension due to left heart disease.&lt;/p&gt;

Cited by 0Open access

Algorithm of selection criteria.

Pablo Rios (777220), Laura Sola (2011744), Alejandro Ferreiro (4237804) et al. · Figshare · 2022

&lt;p&gt;Total population in the National Renal HealthCare Program Uruguay (NRHP-UY), the studied groups distribution and the evolution with follow-up until September 30th 2019.&lt;/p&gt;

Cited by 0Open access