Optimal Ranking Research Articles

Two-stage group ordinal consensus optimization ranking approach considering multiple optimal consistency adjustment solutions

In group decision making, decision makers can use pairwise comparisons to express their preferences. Reaching the consistency of decision makers and consensus of decision groups are two important processes for obtaining reliable decision-making results. Consistency and consensus optimization approach based on minimizing preference adjustment is one of the dominant approaches to obtaining ranking results by deriving priorities. In existing researches, maintaining consistency in preference adjustment during ordinal consensus optimization processes has been rarely noticed, and the choice of the consistency adjusted preferences from multiple optimal solutions has not been considered. To deal with these issues, this paper proposes a two-stage group ordinal consensus optimization ranking approach which considers multiple optimal consistency adjustment solutions to achieve individual preference consistency and group ordinal consensus. In the first stage, a minimum adjustment consistency optimization model is developed to obtain the minimum adjustment based on the concept of strong ordinal consistency. In the second stage, an ordinal consensus optimization ranking model is developed to obtain the optimal consistency and consensus adjustment solutions by minimizing the preference adjustments based on an improved consensus measure. Finally, a numerical example about lithium-ion battery recycling strategy selection is applied to illustrate the feasibility of the proposed approach. Moreover, the comparisons with other approaches are given to show the advantage and the sensitivity analysis is used to verify the effectiveness of the proposed approach.

Optimal ranking model of fuzzy preference relations with self-confidence for addressing self-confidence failure

Fuzzy preference relation with self-confidence (FPR-SC) uses semantic self-confidence to illustrate the hesitation of experts in affirming given preference values. However, extant ranking derivation methods of FPRs-SC suffer from self-confidence failure problem. Specifically, when the logical operations of self-confidence levels are replaced by algebraic operations on semantic subscripts, the derived rankings may be unstable or independent of the self-confidence. To address this issue, an optimal ranking model of FPR-SC with chance-constrained is proposed and extended to group decision-making. Based on the concept of ‘reliability level’ in parameter estimation, the evaluation information expressed by ‘preference values + self-confidence levels’ is first explained using probability distributions to achieve dimensional unity between qualitative self-confidence and quantitative preference. A multiplicative consistency-driven optimal model is then designed to assess the individuality of self-confidence. Guided by the ‘3σ’ principle, FPR-SC is further replaced by random variables following asymmetric bilateral truncated normal distributions. This transformation captures the inner cognition of individuals during subjective judgment, and ensures effective constraints on the numerical range through the asymmetric design. Finally, motivated by the minimization of information deviation, an FPR-SC optimal ranking model with chance-constrained is constructed, and its effectiveness is verified.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

A novel ensemble over-sampling approach based Chebyshev inequality for imbalanced multi-label data

With the development of intelligent technology, data exhibits characteristics of multi-label and imbalanced distribution, which lead to the degradation of classification model performance. Therefore, addressing multi-label class imbalance has become a hot research topic. Nowadays, over-sampling approaches aim to generate a superset of the original dataset to deal with imbalanced data. However, traditional over-sampling methods only employ the central data point and its nearest neighbor samples to synthesize samples without considering the impact of data distribution. To address these issues, in this paper, we propose an ensemble multi-label over-sampling algorithm (MLCIO) based on Chebyshev inequality and a group optimization strategy. Firstly, to generate more representative and diverse samples, with the seed sample serving as the sphere’s center, Chebyshev inequality is utilized to ensure that synthetic samples fall within its m times the standard deviation. Secondly, a group optimization ranking weighting approach is employed to obtain more reliable and stable label information. Finally, comparative experiments are conducted on 11 imbalanced datasets from various domains using different evaluation metrics. The results demonstrate that our proposal achieves better performance than other approaches.

Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model

This paper utilizes a hybrid model to analyze the impression of information from the GECON indicator on the volatility prediction of the clean energy market. The model architecture is constructed by embedding a recurrent neural network (RNN) into the GARCH-MIDAS model. The results show that RNN-GARCH-MIDAS-GECON achieves optimal ranking in volatility prediction. This work confirms the advantages of embedded hybrid integrated models in capturing nonlinear information in financial markets and achieving significant progress in volatility forecasts. Notably, this research will help to promote the construction of clean energy development and energy transition pathways.

The extent of overtourism in some European locations using multi-criteria decision-making methods between 2014 and 2023

Purpose Overtourism has been identified as a significant global problem with numbers of negative externalities. The purpose of this paper is to contribute to the establishment of a standard that objectively measures the extent of tourism to produce a dynamic ranking of selected European settlements, as there is a lack of studies using sophisticated statistical methods to analyse secondary data on overtourism. Design/methodology/approach The 28 selected sites, studied according to their involvement in overtourism, were ranked using multi-criteria decision-Mmaking Methods between 2014 and 2023. Rankings were calculated by VIKOR, TOPSIS and MMOORA, and an aggregate ranking was created by using the cross-entropy optimization. Additionally, the annual changes in the rankings were presented graphically using principal component analysis on a two-dimensional space, referred to as the “sites' space.” Finally, the sites were clustered into three distinct groups based on their level of overtourism: less, medium and more. This was achieved through the use of the K-means algorithm. Findings Following the onset of the pandemic in 2020, there was a notable decline in overtourism. However, following the year 2021, the numbers began to rise once more, and by 2022, they had returned to their pre-pandemic levels. Among the methods, MMOORA demonstrated the most effective performance in comparison to the optimal ranking. Originality/value The applied methods with novel rank aggregation could also shed light on the most relevant indicators to overtourism. These included the number of nights spent in paid accommodation per population, the number of overnight visits per population and the number of air passengers.

Topological indices based VIKOR assisted multi-criteria decision technique for lung disorders.

Lung disorders involve swelling, inflammation, and muscle tightening around the airways, with symptoms such as coughing, wheezing, shortness of breath, and abnormal fluid build-up. The global prevalence of these conditions is rising, highlighting the need for extensive research to alleviate their severity and symptoms. Due to the chronic nature and recurrence of these disorders, the human body often develops immunity and side effects to certain medications. Therefore, developing novel and appropriate drug combinations is crucial. This study analyzes a dataset of lung disorder drugs, characterized by various topological indices. The structures of 16 drugs used to treat lung disorders are correlated with their physical properties using degree-based graph invariants. When considering specific attributes, the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method provides an optimal ranking for each drug. The QSPR results highlight the effectiveness of this approach in drug prioritization, offering valuable insights for clinical decision-making and drug development. This methodology can enhance the strategic selection of treatments for lung disorders, leading to improved patient care and better resource allocation.

Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise

In singular spectrum analysis, which is applied to signal extraction, it is of critical importance to select the number of components correctly in order to accurately estimate the signal. In the case of a low-rank signal, there is a challenge in estimating the signal rank, which is equivalent to selecting the model order. Information criteria are commonly employed to address these issues. However, singular spectrum analysis is not aimed at the exact low-rank approximation of the signal. This makes it an adaptive, fast, and flexible approach. Conventional information criteria are not directly applicable in this context. The paper examines both subspace-based and information criteria, proposing modifications suited to the Hankel structure of trajectory matrices employed in singular spectrum analysis. These modifications are initially developed for white noise, and a version for red noise is also proposed. In the numerical comparisons, a number of scenarios are considered, including the case of signals that are approximated by low-rank signals. This is the most similar to the case of real-world time series. The criteria are compared with each other and with the optimal rank choice that minimizes the signal estimation error. The results of numerical experiments demonstrate that for low-rank signals and noise levels within a region of stable rank detection, the proposed modifications yield accurate estimates of the optimal rank for both white and red noise cases. The method that considers the Hankel structure of the trajectory matrices appears to be a superior approach in many instances. Reasonable model orders are obtained for real-world time series. It is recommended that a transformation be applied to stabilize the variance before estimating the rank.

Decoupling Implantation Prediction and Embryo Ranking in Machine Learning: The Impact of Clinical Data and Discarded Embryos

Automated live embryo imaging has transformed in vitro fertilization (IVF) into a data‐intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Herein, it is established that this strategy can lead to suboptimal selection of embryos. It is revealed that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, it is found that ambiguous labels of failed implantations, due to either low‐quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, conceptual and practical steps are proposed to enhance machine learning‐driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking and reducing label ambiguity.

An AI-Powered Product Identity Form Design Method Based on Shape Grammar and Kansei Engineering: Integrating Midjourney and Grey-AHP-QFD

Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&amp;D). The design of new product forms must ensure the continuity of PI and concurrently address the emotional needs of users. It demands a high level of experience from designers and significant investment in R&amp;D. To solve this problem, a generative and quantitative design method powered by AI, based on Shape Grammar (SG) and Kansei Engineering (KE), is proposed. The specific method is as follows: Firstly, representative products for Morphological Analysis (MA) are selected, SG is applied to establish initial shapes and transformation rules, and prompts are input into Midjourney. This process generates conceptual sketches and iteratively refines them, resulting in a set of conceptual sketches that preserve the PI. Secondly, a web crawler mines online reviews to extract Kansei words. Factor Analysis (FA) clusters them into Kansei factors, and the Grey Analytic Hierarchy Process (G-AHP) calculates their grey weights. Thirdly, after analyzing the PI conceptual sketches for feature extraction, the features are integrated with CRs into the Quality Function Deployment (QFD) matrix. Experts evaluate the relationships using interval grey numbers, calculating the optimal ranking of PI Engineering Characteristics (PIECs). Finally, professional designers refine the selected sketches into 3D models and detailed designs. Using a Chinese brand as a case study, we have designed a female electric moped (E-moped) to fit the PI and users’ emotional needs. Through a questionnaire survey on the design scheme, we argue that the proposed innovative method is efficient, applicable, and effective in balancing the product form design of PI and user emotions.

Grading of Balance Function in Subacute Stroke Patients by Using the Berg Balance Scale Together with Latent Rank Theory.

The Berg Balance Scale (BBS) is a core measure of balance function in patients with stroke. Latent rank theory (LRT) is a statistical method that enables the degree of functional impairment to be ranked from the sub-items of a rating scale; each rank can then be characterized. Identification of the characteristics of balance function by rank would be beneficial for interventions to improve balance function in patients with stroke. This study aims to use LRT to rank and characterize patients with stroke balance impairment. This was a multicenter retrospective analysis of 293 patients with subacute stroke. We used LRT and the BBS to estimate the optimal rankings based on the goodness-of-fit index and the information criterion. We compared the obtained ranks with the level of walking independence for each rank. The evaluation of the patient's BBS scores revealed that balance impairment could be divided into six ranks. The average BBS score for each rank rose from 27.1 for rank 1 to 53.9 for rank 6. The scores of the BBS sub-items for each rank also differed. The level of walking independence by rank ranged from rank 1 for assisted walking to rank 6 for independent outdoor walking. Balance function in patients with subacute stroke was ranked sixth in the BBS, with varying characteristics identified for different ranks. This result helped to determine the therapy to improve the balance function of patients with stroke.

An interior penalty function method for solving fuzzy nonlinear programming problems

In this article, we investigate fuzzy interior penalty function method for solving fuzzy nonlinear programming problems (FNLPP) based on a new fuzzy arith-metic, unconstrained optimization, and fuzzy ranking on the parametric form of triangular fuzzy numbers (TFN). The main objective of this paper is to solve constrained fuzzy nonlinear programming problems using interior penalty func-tions (IPF) by converting it into unconstrained optimization problems. We prove an important lemma and a convergence theorem for the interior penalty functions method. Interior penalty function techniques favor sites near the boundary of the feasible region in the interior. We present a numerical example of the suggested method and compare the results to those produced by existing methods.

Implementing Preference Selection Index for Optimal Employee Ranking in Organizational Decision-Making

The rapid development of information technology has affected various aspects of life, including in the world of work. This research aims to apply the Preference Selection Index (PSI) method in determining the best employees at Bina Karya Utama Company. The assessment is based on four main criteria: Attendance, Tardiness, Overtime, and Length of Service. Data is obtained through observation and interviews, then processed using the PSI method which involves the normalization process and the calculation of preference values. The results showed that employees with alternative code A8 obtained the highest score, followed by A5 and A9. The PSI method proved to be effective in helping companies make objective and fair decisions, as well as motivating employees to improve their performance. This research concludes that a PSI-based decision support system can improve transparency and fairness in employee evaluation at Bina Karya Utama Company.

Research on optimal investment decision of distribution network projects under the construction of the new power system

Abstract This article establishes a ranking model of distribution network projects to make optimal investment decisions. The project optimization ranking model consists of the LCC model and the project comprehensive evaluation model. When constructing the evaluation model of distribution network projects, the evaluation index system is first set, and then the weight allocation for each index is calculated based on the G1-antientropy weight comprehensive integrated weighting method, so as to calculate the comprehensive benefit of the unit cost of a project for optimal sorting. Taking a distribution network planning project in Sichuan Province as an example, the empirical analysis is conducted, and the ranking results are obtained by using the above optimization ranking model for distribution network projects depending on the comprehensive benefit index of unit cost of the project A-E, which confirms the reasonability and practicability of the decision-making system and realizes the optimization of investment decisions for distribution network projects.

Graphs whose \(l_p\)-optimal rankings are \(l_{\infty}\) Optimal

A ranking on a graph G is a function f : V ( G ) → { 1 , 2 , … , k } with the following restriction: if f ( u ) = f ( v ) for any u , v ∈ V ( G ) , then on every u v path in G , there exists a vertex w with f ( w ) &gt; f ( u ) . The optimality of a ranking is conventionally measured in terms of the l ∞ norm of the sequence of labels produced by the ranking. In \cite{jacob2017lp} we compared this conventional notion of optimality with the l p norm of the sequence of labels in the ranking for any p ∈ [ 0 , ∞ ) , showing that for any non-negative integer c and any non-negative real number p , we can find a graph such that the sets of l p -optimal and l ∞ -optimal rankings are disjoint. In this paper we identify some graphs whose set of l p -optimal rankings and set of l ∞ -optimal rankings overlap. In particular, we establish that for paths and cycles, if p &gt; 0 then l p optimality implies l ∞ optimality but not the other way around, while for any complete multipartite graph, l p optimality and l ∞ optimality are equivalent.

Multi-criteria decision-making for the selection of best airport ground access mode with a new fuzzy rough-entropy based method

In order to cope with the inherent complexities and uncertainties of settings such as transportation problems, the adoption of proper decision procedures becomes a pivotal issue. Particularly, multi-criteria group decision-making (MCGDM) uses multiple evaluations of the options based on several criteria to select the optimal choice and/or rank the alternatives. In this paper we are interested in the Entropy-REGIME method for MCGDM and its ability to handle uncertainty in the presence of fuzzy rough numbers (that allow the practitioner to represent and process imprecise data). To this end we propose a hybrid framework of the fuzzy rough-entropy based REGIME method that involves two main components: fuzzy rough-entropy for the evaluation of the objective weights of the decision criteria, and fuzzy rough-REGIME to rank the alternatives based on the weighted criteria. Its motivation is a case study that concerns the selection of the best transport mode to access Istanbul newly constructed airport. We consider four alternatives for ground access options, namely, underground metro, bus rapid transit, light rail transit, and premium bus services. Our findings show that an underground metro system is the optimal mode, followed by light rail transit, bus rapid transit, and premium bus services. Additionally, to demonstrate the superiority and effectiveness of the proposed technique, comparisons are made with existing methods.

Multi-source transfer learning via optimal transport feature ranking for EEG classification

Motor imagery (MI) brain-computer interface (BCI) paradigms have been extensively used in neurological rehabilitation. However, due to the required long calibration time and non-stationary nature of electroencephalogram (EEG) signals, it is challenging to obtain a substantial sample size of EEG data. Transfer learning (TL), which leverages knowledge from existing subjects to enhance the learning performance in new subjects, has been employed to solve this problem. Previous studies often extract sample features directly from the two domains for transfer learning, leading to poor features and negative transfer. To overcome this limitation, an optimal transport feature selection method was developed in this study to select the most suitable features for migration to enhance the generalization capability of the TL. This was achieved by analyzing the diagonal value similarity of the optimal transport coupling matrix. First, the Riemannian tangent space mapping method was used to map the sample covariance matrix of EEG trials from the Riemannian manifold to tangent space. Secondly, the optimal subset of EEG features was selected via the optimal transport feature selection method. Subsequently, the separate distribution alignment method was employed to minimize dissimilarities between the source and target domains. Finally, the weighted voting mechanisms were integrated for decision fusion. The proposed multi-source transfer feature learning (MSTFL) method was validated based on two public BCI Competition IV datasets. Our results demonstrated superior classification accuracy of 85.93% and 79.48%, respectively, on the two public datasets, compared to state-of-the-art models. These findings indicate the effectiveness of our proposed MSTFL model for both feature extraction and classification of MI signals.

Cross-validation to select the optimum rank for a reduced-rank approximation to multivariate data

ABSTRACT In this paper we consider the Gabriel form of cross-validation (CV) and we investigate how to estimate the optimum rank for lower rank approximations of any dataset that can be written in matrix form, with particular application in multivariate analysis and in the analysis of multienvironment trials. The literature related to the method suggests that it can produce overfitting and poor-quality predictions, characteristics that result in overestimation of the rank. Because of this, it is proposed to change the rank selection criterion, testing thirteen statistics both in the original method and in four proposed extensions that seek to solve the above problems. A comparison is made with two gold standard methods for CV through a simulation study and through the analysis of seventeen real datasets, two of which are general multivariate and fifteen are from experiments with genotype-by-environment interaction. It is concluded that from a predictive point of view, the highest accuracy in estimating the rank is obtained by using a regularized singular value decomposition.

A Theory of Socially Responsible Investment

AbstractWe characterize the conditions under which a socially responsible (SR) fund induces firms to reduce externalities, even when profit-seeking capital is in perfectly elastic supply. Such impact requires that the SR fund’s mandate permits the fund to trade off financial performance against reductions in social costs—relative to the counterfactual in which the fund does not invest in a given firm. Based on such an impact mandate, we derive the social profitability index, an investment criterion that characterizes the optimal ranking of impact investments when SR capital is scarce. If firms face binding financial constraints, the optimal way to achieve impact is by enabling a scale increase for clean production. In this case, SR and profit-seeking capital are complementary: Surplus is higher when both investor types are present.

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.