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Journal articles on the topic 'Weighting methods'

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Author: Grafiati

Published: 10 March 2023

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1

Krueger, Thomas, and Mark Wrolstad. "Portfolio Allocation Using Free Cash Flows and Other Methods." Journal of Finance Issues 11, no. 2 (December 31, 2013): 58–67. http://dx.doi.org/10.58886/jfi.v11i2.2518.

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There are many ways to allocate money invested in shares of common stock within one’s portfolio. The traditional and best known allocation methods are price-weighting, market capitalization-weighting, and equal-weighting. Of these three traditional allocation methods, we find that equally-weighted portfolios performed the best. More recently, attention has been focused on “fundamental weightings” which use financial statement items such as sales, total assets, net income, leverage, EBIT, and free cash flows to weight stock portfolio investments. Using a well-known set of stocks, this research provides insight into the relative advantages of using some of these alternative portfolio allocation methods. This study found that using free cash flows to weight portfolios was the only technique that outperformed equally-weighted portfolios and provided the investor with positive, statistically significant returns. It was also found that when using free cash flows to weight the portfolios, levels of free cash flows were more important that trends.

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Niño-Adan, Iratxe, Diana Manjarres, Itziar Landa-Torres, and Eva Portillo. "Feature weighting methods: A review." Expert Systems with Applications 184 (December 2021): 115424. http://dx.doi.org/10.1016/j.eswa.2021.115424.

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3

Barlow, Roger. "Event classification using weighting methods." Journal of Computational Physics 72, no. 1 (September 1987): 202–19. http://dx.doi.org/10.1016/0021-9991(87)90078-7.

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4

Wu, Changbao, and Wilson W. Lu. "Calibration Weighting Methods for Complex Surveys." International Statistical Review 84, no. 1 (March 3, 2015): 79–98. http://dx.doi.org/10.1111/insr.12097.

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5

Jiang, Yihong, and Jianfa Shen. "Weighting for what? A comparison of two weighting methods for measuring urban competitiveness." Habitat International 38 (April 2013): 167–74. http://dx.doi.org/10.1016/j.habitatint.2012.06.003.

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6

Csató, László, and Lajos Rónyai. "Incomplete Pairwise Comparison Matrices and Weighting Methods*." Fundamenta Informaticae 144, no. 3-4 (May 2, 2016): 309–20. http://dx.doi.org/10.3233/fi-2016-1337.

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7

Zaitseva, A., and V. Alopaeus. "Improving Group Contribution Methods by Distance Weighting." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 68, no. 2 (March 2013): 235–47. http://dx.doi.org/10.2516/ogst/2012035.

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8

Rajalakshmi, R. "SUPERVISED TERM WEIGHTING METHODS FOR URL CLASSIFICATION." Journal of Computer Science 10, no. 10 (October 1, 2014): 1969–76. http://dx.doi.org/10.3844/jcssp.2014.1969.1976.

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9

Pöyhönen, Mari, and Raimo P. Hämäläinen. "On the convergence of multiattribute weighting methods." European Journal of Operational Research 129, no. 3 (March 2001): 569–85. http://dx.doi.org/10.1016/s0377-2217(99)00467-1.

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10

Holt, D., and D. Elliot. "Methods of Weighting for Unit Non-Response." Statistician 40, no. 3 (1991): 333. http://dx.doi.org/10.2307/2348286.

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11

Watanabe, Hiroshi, Yoshikazu Miyanaga, Nobuhiro Miki, and Nobuo Nagai. "Arma spectral approximation methods with weighting functions." Electronics and Communications in Japan (Part I: Communications) 68, no. 4 (April 1985): 27–35. http://dx.doi.org/10.1002/ecja.4410680404.

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12

Baldwin, Mark P., David B. Stephenson, and Ian T. Jolliffe. "Spatial Weighting and Iterative Projection Methods for EOFs." Journal of Climate 22, no. 2 (January 15, 2009): 234–43. http://dx.doi.org/10.1175/2008jcli2147.1.

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Abstract Often there is a need to consider spatial weighting in methods for finding spatial patterns in climate data. The focus of this paper is on techniques that maximize variance, such as empirical orthogonal functions (EOFs). A weighting matrix is introduced into a generalized framework for dealing with spatial weighting. One basic principal in the design of the weighting matrix is that the resulting spatial patterns are independent of the grid used to represent the data. A weighting matrix can also be used for other purposes, such as to compensate for the neglect of unrepresented subgrid-scale variance or, in the form of a prewhitening filter, to maximize the signal-to-noise ratio of EOFs. The new methodology is applicable to other types of climate pattern analysis, such as extended EOF analysis and maximum covariance analysis. The increasing availability of large datasets of three-dimensional gridded variables (e.g., reanalysis products and model output) raises special issues for data-reduction methods such as EOFs. Fast, memory-efficient methods are required in order to extract leading EOFs from such large datasets. This study proposes one such approach based on a simple iteration of successive projections of the data onto time series and spatial maps. It is also demonstrated that spatial weighting can be combined with the iterative methods. Throughout the paper, multivariate statistics notation is used, simplifying implementation as matrix commands in high-level computing languages.

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13

Jiang, Ray, Shangtong Zhang, Veronica Chelu, Adam White, and Hado van Hasselt. "Learning Expected Emphatic Traces for Deep RL." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 7015–23. http://dx.doi.org/10.1609/aaai.v36i6.20660.

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Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approximation, such as neural networks, this combination is known as the deadly triad and is potentially unstable. Recently, it has been shown that stability and good performance at scale can be achieved by combining emphatic weightings and multi-step updates. This approach, however, is generally limited to sampling complete trajectories in order, to compute the required emphatic weighting. In this paper we investigate how to combine emphatic weightings with non-sequential, off-line data sampled from a replay buffer. We develop a multi-step emphatic weighting that can be combined with replay, and a time-reversed n-step TD learning algorithm to learn the required emphatic weighting. We show that these state weightings reduce variance compared with prior approaches, while providing convergence guarantees. We tested the approach at scale on Atari 2600 video games, and observed that the new X-ETD(n) agent improved over baseline agents, highlighting both the scalability and broad applicability of our approach.

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14

Davis, Kermit R., and William I. Sauser. "A Comparison of Factor Weighting Methods in Job Evaluation: Implications for Compensation Systems." Public Personnel Management 22, no. 1 (March 1993): 91–106. http://dx.doi.org/10.1177/009102609302200107.

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Job evaluation has received a great deal of attention recently, in part because of its potential role in the gender-related pay equity issue. Investigators have been particularly interested in such psychometric characteristics of job evaluation instruments and plans such as reliability, gender bias, construct validity, and predictive accuracy. One aspect of job evaluation methodology that could directly affect compensation systems is the weighting of factors in scoring jobs. However, very little information exists in the compensation literature about the differential effects of alternative weighting methods or the psychometric parameters that may contribute to such differences. The present article reviews past psychometric research related to weighting, and presents evidence of the salary effects of four different weighting methods examined in an applied job evaluation setting. The study sample consisted of 52 jobs in a municipal government. The four weighting methods were: (1) an unweighted raw score composite, (2) equal unit weights, (3) committee-judgmental weights, and (4) multiple regression weights. Results indicated high agreement among the 4 methods in terms of ordinal rankings of the pay rates for the 52 jobs. However, when jobs were classified into pay grades using the alternative weighting models, distinct differences occurred. Particularly relevant was a finding that the weighting models differed in their relative impact on male and female dominated jobs. The article discusses generalizability issues and recommendations to practitioners concerning weighting methodology in job evaluation projects.

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15

Roszkowska, Ewa. "Rank Ordering Criteria Weighting Methods – a Comparative Overview." Optimum. Studia Ekonomiczne, no. 5(65) (2013): 14–33. http://dx.doi.org/10.15290/ose.2013.05.65.02.

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16

Aamodt, Michael G., and Wilson W. Kimbrough. "Comparison of Four Methods for Weighting Multiple Predictors." Educational and Psychological Measurement 45, no. 3 (September 1985): 477–82. http://dx.doi.org/10.1177/001316448504500305.

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17

Imai, Kosuke. "Comments: Improving Weighting Methods for Causal Mediation Analysis." Journal of Research on Educational Effectiveness 5, no. 3 (July 2012): 293–95. http://dx.doi.org/10.1080/19345747.2012.688460.

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18

Johnsen, Fredrik Moltu, and Søren Løkke. "Review of criteria for evaluating LCA weighting methods." International Journal of Life Cycle Assessment 18, no. 4 (September 15, 2012): 840–49. http://dx.doi.org/10.1007/s11367-012-0491-y.

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19

Holt, D., and D. Elliot. "Corrigendum: Methods of Weighting for Unit Non-Response." Statistician 41, no. 5 (1992): 599. http://dx.doi.org/10.2307/2348925.

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20

Sackett, Paul R., Jeffrey A. Dahlke, Oren R. Shewach, and Nathan R. Kuncel. "Effects of predictor weighting methods on incremental validity." Journal of Applied Psychology 102, no. 10 (October 2017): 1421–34. http://dx.doi.org/10.1037/apl0000235.

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21

Odu, G. O. "Weighting methods for multi-criteria decision making technique." Journal of Applied Sciences and Environmental Management 23, no. 8 (September 11, 2019): 1449. http://dx.doi.org/10.4314/jasem.v23i8.7.

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22

Todd, Michael J. "Scaling, shifting and weighting in interior-point methods." Computational Optimization and Applications 3, no. 4 (October 1994): 305–15. http://dx.doi.org/10.1007/bf01299206.

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23

Teegavarapu, Ramesh S. V. "Precipitation imputation with probability space-based weighting methods." Journal of Hydrology 581 (February 2020): 124447. http://dx.doi.org/10.1016/j.jhydrol.2019.124447.

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24

Ji, Ai Min, Kun Zhu, and Quan Sheng Huang. "Methods Determining the Weights of Characteristics in Mechanical Products Design on Case-Base Reasoning." Applied Mechanics and Materials 138-139 (November 2011): 315–20. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.315.

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In Case-Based Reasoning (CBR), the determination of the weights of case characteristics is the base of the accurate case match, even influences the CBR execution. The current methods of determining the weights of case characteristics include subjective and objective weighting method but, some defects exist in them. In order to decrease subjectivity and increase objectivity of weight determining, the paper presented a new kind of combination weighting method that is the combination of the Analytic Hierarchy Process (AHP) with roughness set theory. The weighting process about AHP, roughness set theory, and combination method was described in detail respectively. An example was given to verify the effectiveness and accuracy of the combination weighting method.

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25

Li, Yan, and Liang Li. "Propensity score analysis methods with balancing constraints: A Monte Carlo study." Statistical Methods in Medical Research 30, no. 4 (February 1, 2021): 1119–42. http://dx.doi.org/10.1177/0962280220983512.

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The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We compared simple inverse probability weighting, two propensity score weighting methods with balancing constraints (covariate balancing propensity score, covariate balancing scoring rule), and two weighting methods with balancing constraints but without using the propensity scores (entropy balancing and kernel balancing). We observed that correct specification of the propensity score model remains important even when the constraints effectively balance the covariates. We also observed evidence suggesting that, with similar covariate balance constraints, the use of a propensity score model improves the estimation performance when the dimension of covariates is large. These findings suggest that it is important to develop flexible data-driven propensity score models that satisfy covariate balancing conditions.

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26

Xu, Haikun, James T. Thorson, and Richard D. Methot. "Comparing the performance of three data-weighting methods when allowing for time-varying selectivity." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 2 (February 2020): 247–63. http://dx.doi.org/10.1139/cjfas-2019-0107.

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How to properly weight composition data is an important ongoing research topic for fisheries stock assessments, and multiple methods for weighting composition data have been developed. Although several studies indicated that properly accounting for time-varying selectivity can reduce estimation biases in population biomass and management-related quantities, no study to date has compared the performance of widely used data-weighting methods when allowing for time-varying selectivity. Here, we conducted four simulation experiments on this topic, aiming to provide guidance on weighting age-composition data given time-varying selectivity. The first simulation experiment showed that over-weighting should be avoided in general and even when estimating time-varying selectivity. The second simulation experiment compared three data-weighting methods (McAllister–Ianelli, Francis, and Dirichlet-multinomial), within which the Dirichlet-multinomial method outperformed the other two methods when selectivity is time-varying. The third and fourth simulation experiments further showed that given time-varying selectivity, the Dirichlet-multinomial method still performed well when age-composition data were over-dispersed and when the level of selectivity variation needed to be simultaneously estimated. Our simulation results support using the Dirichlet-multinomial method when estimating time-varying fishery selectivity. Also, the simulations suggest that improving stock assessments by accounting for time-varying selectivity requires simultaneously addressing data weighting and time-varying selectivity.

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27

Daniawan, Benny. "Evaluation of Lecturer Teaching Performance Using AHP and SAW Methods." bit-Tech 1, no. 2 (December 17, 2018): 30–39. http://dx.doi.org/10.32877/bt.v1i2.41.

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The lecturer performance evaluation activity is the routine of an university in continuously improving internal quality as an evaluation and development of educational institutions. Buddhi Dharma University Tangerang, every semester evaluates lecturers' performance. But the results obtained are not optimal, this is due to the absence of an effective and efficient method in determining the results, especially in the Faculty of Science and Technology, Information System Departement. The assessment process is carried out by distributing questionnaire papers and filled out by students. This study aims to analyze the results of the questionnaire, calculated by combining the Analytical Hierarchy Process (AHP) method for weighting and combined the Simple Addictive Weighting (SAW) method for ranking. The results obtained were the level of criteria weighting accuracy reached 90.39% with 28 lecturers which teaching 47 subjects in the Information Systems Departement.

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28

Tian, Mingxing, Yaomin Wang, and Jun Li. "Comprehensive Harmonic Responsibility Calculation Based on Different Weighting Methods." Energies 12, no. 23 (November 22, 2019): 4449. http://dx.doi.org/10.3390/en12234449.

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Dividing the responsibility of harmonics between customer and utility sides is necessary to improve the objectivity, rationality, and scientificalness of power measurement and power quality evaluation. On the basis of expert experience and customer needs and considering the background harmonic fluctuation, this study proposes two reasonable conditions to evaluate harmonic responsibility. At the same time, the reference index of harmonic responsibility calculation and the problem of different frequency harmonics are considered in the comprehensive calculation. On the basis of the IEEE Std. 1459–2010 power theory, the index set of harmonic responsibility is established, and several common subjective and objective weighting methods are used to weigh each index. On the basis of optimization theory, an optimization model is established by constructing a Lagrange function in finding the condition extremum to unify the subjective and objective information. Finally, the calculation method is verified by a Norton equivalent model, and the harmonic responsibility of each index is calculated by the harmonic current vector method; thus, calculating the actual impedance of customer and utility sides is unnecessary, making the calculation simple and effective. Results of the comparative analysis show that the comprehensive evaluation method of harmonic responsibility with the combinatorial weighting method can not only meet the different needs of different groups for harmonic indicators but also reflect the background harmonic fluctuations sensitively. In this manner, the subjective and objective information are unified, and the shortcomings of a single weighting method are overcome. Hence, the evaluation results are accurate, practical, and reasonable.

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Rota, Bernardo Jo˜ao, and Thomas Laitila. "Comparisons of Some Weighting Methods for Non-response Adjustment." Lietuvos statistikos darbai 54, no. 1 (December 20, 2015): 69–83. http://dx.doi.org/10.15388/ljs.2015.13882.

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Sample and population auxiliary information have been demonstrated to be useful and yield approximately equal resultsin large samples. Several functional forms of weights are suggested in the literature. This paper studies the properties of calibrationestimators when the functional form of response probability is assumed to be known. The focus is on the difference between populationand sample level auxiliary information, the latter being demonstrated to be more appropriate for estimating the coefficients in theresponse probability model. Results also suggest a two-step procedure, using sample information for model coefficient estimation inthe first step and calibration estimation of the study variable total in the second step.

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30

LUND, JAY R. "UTILITY THEORY VIOLATIONS BY MULTI-CRITERIA HIERARCHICAL WEIGHTING METHODS." Civil Engineering Systems 11, no. 3 (December 1994): 197–207. http://dx.doi.org/10.1080/02630259408970146.

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31

Delle Site, Paolo, and Francesco Filippi. "Weighting methods in multi-attribute assessment of transport projects." European Transport Research Review 1, no. 4 (December 2009): 199–206. http://dx.doi.org/10.1007/s12544-009-0018-1.

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32

Orăsan, Constantin. "Comparative Evaluation of Term-Weighting Methods for Automatic Summarization*." Journal of Quantitative Linguistics 16, no. 1 (February 2009): 67–95. http://dx.doi.org/10.1080/09296170802514187.

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33

Wootten, Adrienne M., Elias C. Massoud, Duane E. Waliser, and Huikyo Lee. "Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States." Earth System Dynamics 14, no. 1 (February 3, 2023): 121–45. http://dx.doi.org/10.5194/esd-14-121-2023.

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Abstract. Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.

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34

Cebi, Selcuk, and Cengiz Kahraman. "A new weighted fuzzy information axiom method in production research." Journal of Enterprise Information Management 32, no. 1 (February 11, 2019): 170–90. http://dx.doi.org/10.1108/jeim-03-2017-0046.

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Purpose The purpose of this paper is to propose a novel weighting algorithm for fuzzy information axiom (IA) and to apply it to the evaluation process of 3D printers. Design/methodology/approach As a decision-making tool, IA method is presented to evaluate the performance of any design. Then, weighted IA methods are investigated and a new weighting procedure is introduced to the literature. Then, the existing axiomatic design methods and the proposed new method are classified into two groups: weighting based on information content and weighting based on design ranges. The weighting based on information content approach consists of four methods including pessimistic and optimistic approaches. The philosophy of the weighting based on design ranges is to narrow design ranges in order to decrease fuzziness in the model. To prove the robustness and the performance of the proposed weighting method, the results are compared with the existing methods in the literature. Then, the new approach is applied to evaluate 3D printers. Findings The results of the proposed study show that the proposed weighting algorithm has better performance than the old ones for IA. Therefore, the proposed weighting algorithm should be used for the weighting tool of IA thereafter. Originality/value An effective weighting method compatible with the philosophy of IA method has been proposed. Furthermore, the performances of 3D printers are compared by using the proposed method.

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35

Tao, Wuyong, Xianghong Hua, Kegen Yu, Ruisheng Wang, and Xiaoxing He. "A Comparative Study of Weighting Methods for Local Reference Frame." Applied Sciences 10, no. 9 (May 6, 2020): 3223. http://dx.doi.org/10.3390/app10093223.

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In the field of photogrammetric engineering, computer vision, and graphics, local shape description is an active research area. A wide variety of local shape descriptors (LSDs) have been designed for different applications, such as shape retrieval, object recognition, and 3D registration. The local reference frame (LRF) is an important component of the LSD. Its repeatability and robustness directly influence the descriptiveness and robustness of the LSD. Several weighting methods have been proposed to improve the repeatability and robustness of the LRF. However, no comprehensive comparison has been implemented to evaluate their performance under different data modalities and nuisances. In this paper, we focus on the comparison of weighting methods by using six datasets with different data modalities and application contexts. We evaluate the repeatability of the LRF under different nuisances, including occlusion, clutter, partial overlap, varying support radii, Gaussian noise, shot noise, point density variation, and keypoint localization error. Through the experiments, the traits, advantages, and disadvantages of weighting methods are summarized.

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36

Abdul Halim, Norfazilah Binti, Aliff Radzuan Bin Mohamad Radzi, Nor Zalina Binti Kasim, and Faiz Bin Mohd Turan. "A Systematic Literature Review of Multi-Criteria Analysis Model Methods on Sustainability Weighting for Ethanol Plant." ASM Science Journal 16 (December 23, 2021): 1–13. http://dx.doi.org/10.32802/asmscj.2021.850.

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The sustainability weighting is crucial as it is practically implemented into sustainability evaluation, especially in industrial development. Sustainability is about the interconnection between three aspects of sustainability impact such as economic impact, environmental impact, and social impact. Multi-Criteria Analysis (MCA) model play important roles to measure the weighting for each impact according to the scenario and criteria selected based on scientific rules and robust statistical methods. However, there were insufficient studies on the existing literature sustainability weighting model from MCA method for the ethanol plant. Hence, the present paper demonstrates a systematic literature review of MCA model methods on sustainability weighting for the ethanol plant. There are two steps involved in systematic literature reviews: formulation of the research question and systematic searching strategies consisting of identification, screening, eligibility, quality appraisal, data abstraction and analysis. The review is based on leading databases; Scopus – ScienceDirect, Springer, Taylor and Francis, and one supporting database – Google Scholar. From the review, the preferable MCA weighting model for sustainability evaluation of ethanol plants is ‘integrated Analytical Hierarchy Process (AHP)’ rather than ‘standalone AHP’. The paper offered a significant contribution to the body of knowledge and sustainability evaluation purposes.

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37

Nguyen, Nancy Duong, Órlaith Burke, and Patrick Murphy. "A Simulation Study of Weighting Methods to Improve Labour-Force Estimates of Immigrants in Ireland." Journal of Official Statistics 32, no. 3 (September 1, 2016): 693–718. http://dx.doi.org/10.1515/jos-2016-0035.

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Abstract As immigration has become a global phenomenon in recent years, a number of European countries, including Ireland, have experienced an influx of immigrants, causing a shift in their national demographics. Therefore, it is important that the EU-LFS yield reliable labour-force estimates not only for the whole population, but also for the immigrant population. This article uses simulation techniques to compare the effectiveness of four different weighting mechanisms in order to improve the precision of the labour-force estimates from the Irish component of the European Union Labour Force Survey (EU-LFS) called the Quarterly National Household Survey (QNHS). The four weighting methodologies for comparison include the original and the current weighting scheme of the QNHS as well as our two proposed alternative weighting schemes. The simulation results show that by modifying the current QNHS weighting mechanism, we can improve the accuracy of the labour-force estimates of the immigrant population in Ireland without affecting the estimates of the whole population and the Irish nationals. This article highlights potential issues that other countries with new immigrant populations may face when using the EU-LFS for immigration research, and our recommendations may be useful to researchers and national statistical offices in such countries.

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38

Gómez-Limón, José, Manuel Arriaza, and M. Guerrero-Baena. "Building a Composite Indicator to Measure Environmental Sustainability Using Alternative Weighting Methods." Sustainability 12, no. 11 (May 27, 2020): 4398. http://dx.doi.org/10.3390/su12114398.

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Environmental sustainability in agriculture can be measured through the construction of composite indicators. However, this is a challenging task because these indexes are heavily dependent on how the individual base indicators are weighted. The main aim of this paper is to contribute to the existing literature regarding the robustness of subjective (based on experts’ opinions) weighting methods when constructing a composite indicator for measuring environmental sustainability at the farm level. In particular, the study analyzes two multi-criteria techniques, the analytic hierarchy process and the recently developed best-worst method, as well as the more straightforward point allocation method. These alternative methods have been implemented to empirically assess the environmental performance of irrigated olive farms in Spain. Data for this case study were collected from a panel of 22 experts and a survey of 99 farms. The results obtained suggest that there are no statistically significant differences in the weights of the individual base indicators derived from the three weighting methods considered. Moreover, the ranking of the sampled farms, in terms of their level of environmental sustainability measured through the composite indicators proposed, is not dependent on the use of the different weighting methods. Thus, the results support the robustness of the three weighting methods considered.

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39

Megawaty, Megawaty, and Maria Ulfa. "Decision Support System Methods: A Review." Journal of Information Systems and Informatics 2, no. 1 (March 17, 2020): 192–201. http://dx.doi.org/10.33557/journalisi.v2i1.63.

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The current information system is the backbone of most companies or agencies. Currently there are various types of information systems including "transaction processing systems (TPS), office automation systems (OAS), knowledge work systems (KWS), management information systems (MIS), decision support systems (DSS), expert systems, groups decision support systems (GDSS), computer support collaborative work systems (CSCW), and executive support systems (ESS) ". Of the various types of information systems that are currently available decision support systems are widely used as aids in making decisions. For this reason, in this study, a study was conducted with a method that can be used in a decision support system. From the study conducted there are three decision support system methods that are often used, namely simple additive weighting (SAW), analytic hierarchy process (AHP), and simple multi attribute rating technique (SMART). The third method is a method that has a weighting value and criteria in the calculation process

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40

Ma, Baojun, Hua Yuan, and Ye Wu. "Exploring performance of clustering methods on document sentiment analysis." Journal of Information Science 43, no. 1 (July 10, 2016): 54–74. http://dx.doi.org/10.1177/0165551515617374.

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Clustering is a powerful unsupervised tool for sentiment analysis from text. However, the clustering results may be affected by any step of the clustering process, such as data pre-processing strategy, term weighting method in Vector Space Model and clustering algorithm. This paper presents the results of an experimental study of some common clustering techniques with respect to the task of sentiment analysis. Different from previous studies, in particular, we investigate the combination effects of these factors with a series of comprehensive experimental studies. The experimental results indicate that, first, the K-means-type clustering algorithms show clear advantages on balanced review datasets, while performing rather poorly on unbalanced datasets by considering clustering accuracy. Second, the comparatively newly designed weighting models are better than the traditional weighting models for sentiment clustering on both balanced and unbalanced datasets. Furthermore, adjective and adverb words extraction strategy can offer obvious improvements on clustering performance, while strategies of adopting stemming and stopword removal will bring negative influences on sentiment clustering. The experimental results would be valuable for both the study and usage of clustering methods in online review sentiment analysis.

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Kato, Takeshi, Yasuhiro Asa, and Misa Owa. "Positionality-Weighted Aggregation Methods for Cumulative Voting." International Journal of Social Science Studies 9, no. 2 (February 23, 2021): 79. http://dx.doi.org/10.11114/ijsss.v9i2.5171.

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Respecting minority opinions is vital in solving social problems. However, minority opinions are often ignored in general majority rules. To build consensus on pluralistic values and make social choices that consider minority opinions, we propose aggregation methods that give weighting to the minority's positionality on cardinal cumulative voting. Based on quadratic and linear voting, we formulated three weighted aggregation methods that differ in the ratio of votes to cumulative points and the weighting of the minority to all members, and assuming that the distributions of votes follow normal distributions, we calculated the frequency distributions of the aggregation results. We found that minority opinions are more likely to be reflected proportionately to the average of the distribution in two of the above three methods. This implies that Sen and Gotoh's idea of considering the social position of unfortunate people on ordinal ranking in the welfare economics, was illustrated by weighting the minority's positionality on cardinal voting. In addition, it is possible to visualize the number and positionality of the minority from the analysis of the aggregation results. These results will be useful to promote mutual understanding between the majority and minority by interactively visualizing the contents of the proposed aggregation methods in the consensus-building process. With the further development of information technology, the consensus building based on big data will be necessary. We recommend the use of our proposed aggregation methods to make social choices for pluralistic values such as social, environmental, and economic.

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Bayazıt Subaşı, Ayşe, and Elçin Filiz Taş. "Single Score Environmental Performances of Roof Coverings." Sustainability 15, no. 5 (March 1, 2023): 4387. http://dx.doi.org/10.3390/su15054387.

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Buildings and constructions are responsible for a great amount of global energy and energy-related carbon dioxide emissions. Because of these negative impacts, there is an increase in Life cycle assessment research in the construction sector to measure these effects and evaluate the sustainability performances. Life cycle assessment is a tool that can facilitate the decision-making process in the construction sector for material selection, or for the selection of the best environmentally friendly option in the building component level or building level. In this study, a comparative life cycle assessment analysis is conducted among 12 roof coverings of 1 square meter in the 60-year lifetime of a building. Impact categories that are available in environmental product declarations and included in this study are the global warming potential, ozone depletion potential, acidification potential, eutrophication potential, photochemical ozone creation potential, abiotic depletion potential of non-fossils and abiotic depletion potential of fossils resources. To facilitate the decision-making process, panel and monetary weightings are applied to convert environmental product declaration data of seven impact categories into one single-score. Monetary weightings applied in the study are in Euro 2019, differentiating itself from other comparative life cycle assessment studies. The single-score results are ranked and compared. R04 has the best performance for all panel weightings, while for monetary weightings, R03, R07 and R08 have the best performance for EPS, MMG and EVR, respectively. As a result, for 12 roof coverings, the weighted results could not address one single roof-covering material for numerous reasons. Among the weighting methods, panel weighting sets show more similarity in ranking results, while monetary-weighting sets results are more diverse.

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Rahman, Ashfaqur, and Manzur Murshed. "Feature Weighting and Retrieval Methods for Dynamic Texture Motion Features." International Journal of Computational Intelligence Systems 2, no. 1 (2009): 27. http://dx.doi.org/10.2991/jnmp.2009.2.1.4.

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Man Lan, Chew Lim Tan, Jian Su, and Yue Lu. "Supervised and Traditional Term Weighting Methods for Automatic Text Categorization." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 4 (April 2009): 721–35. http://dx.doi.org/10.1109/tpami.2008.110.

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Tan, Zhiqiang. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables." Journal of the American Statistical Association 101, no. 476 (December 1, 2006): 1607–18. http://dx.doi.org/10.1198/016214505000001366.

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Hong, Guanglei, and Takako Nomi. "Weighting Methods for Assessing Policy Effects Mediated by Peer Change." Journal of Research on Educational Effectiveness 5, no. 3 (July 2012): 261–89. http://dx.doi.org/10.1080/15348431.2012.688421.

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Tang, Zhong, Wenqiang Li, Yan Li, Wu Zhao, and Song Li. "Several alternative term weighting methods for text representation and classification." Knowledge-Based Systems 207 (November 2020): 106399. http://dx.doi.org/10.1016/j.knosys.2020.106399.

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Cao, Zhi-Hao, and Zhong-Yun Liu. "Convergence of relaxed parallel multisplitting methods with different weighting schemes." Applied Mathematics and Computation 106, no. 2-3 (December 1999): 181–96. http://dx.doi.org/10.1016/s0096-3003(98)10120-0.

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Misawa, Kazuharu, and Fumio Tajima. "New Weighting Methods for Phylogenetic Tree Reconstruction Using Multiple Loci." Journal of Molecular Evolution 75, no. 1-2 (August 2012): 1–10. http://dx.doi.org/10.1007/s00239-012-9513-4.

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Astarita, T. "Analysis of weighting windows for image deformation methods in PIV." Experiments in Fluids 43, no. 6 (September 14, 2007): 859–72. http://dx.doi.org/10.1007/s00348-007-0314-2.

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