2018
DOI: 10.1016/j.ejor.2017.12.011
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Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks

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Cited by 15 publications
(5 citation statements)
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“…The main limitation of this study was the inability of the proposed methodology to cope with large dimensions with more data, and due to this, it is suggested to apply big data analytics methods (Ayyıldız et al, 2018 ; Zdravevski et al, 2020 ) and heuristic/meta-heuristic algorithms (Alinaghian et al, 2020 ) for future research opportunities. Furthermore, other uncertainty techniques such as fuzzy programming (Tirkolaee, Abbasian, et al, 2020; Tirkolaee, Goli, et al, 2020 ), robust scenario-based (Chen et al, 2019 ; Homayouni et al, 2021 ; Lotfi et al, 2021a , 2021b ; Lotfi et al, 2020 ; Özmen et al, 2017 ), optimal control (Savku & Weber, 2018 ) and artificial neural network (Graczyk-Kucharska et al, 2020 ) can be implemented and compared with the proposed RO approach.…”
Section: Discussionmentioning
confidence: 99%
“…The main limitation of this study was the inability of the proposed methodology to cope with large dimensions with more data, and due to this, it is suggested to apply big data analytics methods (Ayyıldız et al, 2018 ; Zdravevski et al, 2020 ) and heuristic/meta-heuristic algorithms (Alinaghian et al, 2020 ) for future research opportunities. Furthermore, other uncertainty techniques such as fuzzy programming (Tirkolaee, Abbasian, et al, 2020; Tirkolaee, Goli, et al, 2020 ), robust scenario-based (Chen et al, 2019 ; Homayouni et al, 2021 ; Lotfi et al, 2021a , 2021b ; Lotfi et al, 2020 ; Özmen et al, 2017 ), optimal control (Savku & Weber, 2018 ) and artificial neural network (Graczyk-Kucharska et al, 2020 ) can be implemented and compared with the proposed RO approach.…”
Section: Discussionmentioning
confidence: 99%
“…This paper is devoted to the foundations of semialgebraic gene-environment networks, i.e., to bases of the future development of algorithmic methods to assess those networks and to real-work applications. Already today we may mention that the following refined classes of techniques can be naturally suggested for our new class of networks under uncertainty, for their identification, optimization and extension: (i) Tchebychev Approximation [53], (ii) Semi-Infinite Optimization [29], (iii) Generalized Semi-Infinite Optimization [48,49,56], (iv) Bi-level and Multilevel Optimization [36], (v) Disjunctive Optimization [2], (vi) Robust Optimization [24,39,42], (vii) Conic Optimization [4], (viii) Optimal Control [1], and (ix) Stochastic Optimal Control [33]. Concerning classes of future real-world applications we would like to recommend emerging challenges of, for example, (a) Collaborative Games under Ellipsoidal Uncertainty or (per inner or outer approximations) Hypercube Uncertainty [11,52], (b) Transportation ("Piano Mover's" and many more) problems [32,40], (c) Supply Chain and Inventory Management [12,20,30,41,43,57] Production Planning [38], various kinds of (d) Design problems [46], (e) Artificial Intelligence and Machine Learning (e.g., "Infinite Kernel Learning") [6,13,28], and (f ) Finance, Actuarial Sciences and Pension Fund Systems [14,19,55].…”
Section: Definition 52 a Parameter-dependent Target-environment Netmentioning
confidence: 99%
“…The backward stage process is applied to prevent overfitting from this complex model, the best model is also obtained by removing some basis functions which indicate a small increase in the residual square error [19][20].…”
Section: Multivariate Adaptive Regression Splinesmentioning
confidence: 99%