scholarly journals Model Sistem Pendukung Keputusan Kelompok untuk Penilaian Gangguan Depresii, Kecemasan dan Stress Berdasarkan DASS-42

2020 ◽  
Vol 7 (2) ◽  
pp. 219
Author(s):  
Sri Kusumadewi ◽  
Hepi Wahyuningsih
Keyword(s):  
Decision Maker ◽  
Preference Relation ◽  
Decision Makers ◽  
Stress Disorders ◽  
Weighted Averaging ◽  
Group Decision Support System ◽  
Owa Operator ◽  
Depression And Anxiety ◽  
Test Results ◽  
Ordered Weighted Averaging

<p>Depresi, kecemasan dan stress merupakan tiga gangguan yang sering dijumpai di masyarakat. Ketiga gangguan tersebut memiliki gejala yang hampir mirip. <em>Depression, Anxiety and Stress Scales</em> (DASS) merupakan salah satu alat ukur yang dapat digunakan untuk mengukur tingkat keparahan ketiga gangguan tersebut. DASS dengan jumlah item/gejala sebanyak 42 item dikenal dengan nama DASS-42. Alat ukut ini membedakan dengan jelas item/gejala dari setiap gangguan. Setiap gangguan memiliki item yang mempengaruhi sebanyak 14 item. Pada penelitian ini dibangun model Sistem Pendukung Keputusan Kelompok (SPKK) yang memungkinkan para psikolog untuk berkolaborasi memberikan preferensi terkait prioritas gangguan yang akan terjadi apabila diketahui item/gejala tertentu menurut DASS-42. Preferensi diberikan dengan format <em>ordered vectors</em>. Untuk memudahkan proses agregasi/komposisi, selanjutnya dilakukan transformasi preferensi ke relasi preferensi fuzzy. Operator <em>Ordered Weighted Averaging</em> (OWA) digunakan untuk melakukan agregasi peferensi menjadi satu matriks. Proses seleksi alternatif terbaik dilakukan dengan menggunakan <em>Quantifier Guided Dominance Degree</em> (QGDD). Hasil pengujian menunjukkan bahwa ketepatan hasil SPKK terhadap DASS-42 adalah sebesar 71,43% (30 dari 42 item/gejala). Item/gejala yang beririsan secara signifikan antara gangguan kecemasan dan stress sebesar 16,67%. (7 dari 42), antara depresi dan kecemasan sebesar 9,52% (4 dari 42). Secara umum SPKK ini mampu mengakomodasi preferensi para pengambil keputusan dalam memberikan bobot pengaruh. Gangguan kecemasan dan gangguan stress memiliki gejala yang sangat mirip sehingga untuk beberapa item.gejala pada DASS-42 ada perbedaan yang cukup signifikan.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Depression, anxiety and stress are three disorders that are often found in the community. These three disorders have almost identical symptoms. Depression, Anxiety and Stress Scales (DASS) is an psychological instrument that can be used to measure the severity of these disorders. DASS with a total of 42 items known as DASS-42. This instrument distinguishes clearly the symptoms of each disorder. Each disorder has 14 items affect. The three disorders have a number of symptoms that are similar, even a symptom may affect two or three disorders with different levels of influence. In this study, a Group Decision Support System (GDSS) model was developed so that psychologists can collaborate to give preference regarding priority of disorders that would occur if certain items / symptoms were identified by  DASS-42. Preferences are given in ordered vectors format. The preferences given by each decision maker aggregated to get a single preference. These preferences will be transformed to the fuzzy preference relation format. Ordered Weighted Averaging (OWA) operator used to aggregation process for all decision maker preference. The OWA operator are used to aggregate into one matrix. The best alternative selected by using Quantifier Guided Dominance Degree (QGDD). The test results show that the accuracy of the GDSS results on DASS-42 is 71.43% (30 of 42 items / symptoms). Symptoms that overlap significantly between anxiety and stress disorders are 16.67%. (7 of 42), between depression and anxiety by 9.52% (4 of 42). The GDSS is able to accommodate the preferences of decision makers in giving influence weight. Anxiety and stress disorder have very similar symptoms so that for some symptoms in the DASS-42 there are significant differences.</em></p><p><em><strong><br /></strong></em></p>

Multi-Criteria Decision Making Analysis Based on Target-Oriented OWA Operator

2020 ◽  
Vol 28 (06) ◽  
pp. 911-938
Author(s):  
Meimei Xia
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Weight Vector ◽  
Decision Makers ◽  
Weighted Averaging ◽  
Multi Criteria Decision Making ◽  
Owa Operator ◽  
Ordered Weighted Averaging ◽  
The Impact

The target-oriented multi-criteria decision making is investigated based on the ordered weighted averaging (OWA) operator. The criteria evaluations are measured by using the likelihood of satisfying the targets of criteria. To aggregate the target-oriented criteria evaluations, the target-oriented OWA operator is firstly introduced, in which the target-oriented criteria evaluations are reordered and then aggregated by using the weight vector associated with the position of criteria evaluations. Four types of targets about criteria evaluations and four types of attitudinal characters about criteria weight vector are introduced, based on which, models are given to identify the potential best alternative(s), and estimate the ranges of attitudinal characters about criteria weight vector for each potential best alternative. The proposed models can not only analyze the sensitivity of each potential best alternative, but also can explore the impact of targets about criteria evaluations and attitudinal characters about criteria weight vector on the decision results. Models are further established to find the best and worst ranking orders of each alternative based on targets about criteria evaluations, and give decision analysis by considering specific ranking orders of alternatives. The proposed method considers the targets about criteria evaluations and attitudinal character about criteria weight vector at the same time and can provide decision makers more choices. Several examples are given to illustrate the proposed methods.

scholarly journals A novel extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers using $(\alpha,\beta,\gamma)$-Cuts

2021 ◽  
Author(s):  
Muhammad Touqeer ◽  
Rimsha Umer ◽  
Ali Ahmadian ◽  
Soheil salahshour
Keyword(s):  
Decision Makers ◽  
Decision Problems ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Topsis Method ◽  
Ordered Weighted Averaging ◽  
Novel Approach ◽  
Order Preference ◽  
Interval Type

Multi-criteria decision-making (MCDM) is concerned with structuring and solving decision problems involving multiple criteria for decision-makers in vague and inadequate environment. The “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) is one of the mainly used tactic to deal with MCDM setbacks. In this article, we put forward an extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers (IT2TrNNs) using the concept of (α, β, γ)-cut. First, we present a novel approach to compute the distance between two IT2TrNNs using ordered weighted averaging (OWA) operator and (α, β, γ)-cut. Subsequently, we broaden the TOPSIS method in the context of IT2TrNNs and implemented it on a MCDM problem. Lastly, a constructive demonstration and several contrasts with the other prevailing techniques are employed to articulate the practicability of the proposed technique. The presented strategy yields a flexible solution for MCDM problems by considering the attitudes and perspectives of the decision-makers.

scholarly journals A Study of OWA Operators Learned in Convolutional Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Practical Method ◽  
Local Information ◽  
Weighted Averaging ◽  
Global Information ◽  
Owa Operator ◽  
Owa Operators ◽  
Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

scholarly journals A method for decision making with the OWA operator

2012 ◽  
Vol 9 (1) ◽  
pp. 357-380 ◽  
Author(s):  
José Merigó ◽  
Anna Gil-Lafuente
Keyword(s):  
Decision Making ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Minimum Level ◽  
Ordered Weighted Averaging ◽  
Special Cases ◽  
Wide Range ◽  
Decision Making Problem ◽  
Parameterized Family

A new method for decision making that uses the ordered weighted averaging (OWA) operator in the aggregation of the information is presented. It is used a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index is based on distance measures and other techniques that are useful for decision making. By using the OWA operator in the IMAM, we form a new aggregation operator that we call the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. The main advantage is that it provides a parameterized family of aggregation operators between the minimum and the maximum and a wide range of special cases. Then, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. A further extension of this approach is presented by using hybrid averages and Choquet integrals. We also develop an application of the new approach in a multi-person decision-making problem regarding the selection of strategies.

scholarly journals Three Ways of Defining Owa Operator on the Set of All Normal Convex Fuzzy Sets

2017 ◽  
Vol 69 (1) ◽  
pp. 101-118
Author(s):  
Zdenko Takáč
Keyword(s):  
Fuzzy Sets ◽  
Linear Order ◽  
Recent Literature ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Main Obstacle ◽  
Owa Operators ◽  
Ordered Weighted Averaging ◽  
Algebraic Properties

Abstract We deal with an extension of ordered weighted averaging (OWA, for short) operators to the set of all normal convex fuzzy sets in [0, 1]. The main obstacle to achieve this goal is the non-existence of a linear order for fuzzy sets. Three ways of dealing with the lack of a linear order on some set and defining OWA operators on the set appeared in the recent literature. We adapt the three approaches for the set of all normal convex fuzzy sets in [0, 1] and study their properties. It is shown that each of the three approaches leads to operator with desired algebraic properties, and two of them are also linear.

OWA aggregation with dual preferences for basic uncertain information

2021 ◽  
pp. 1-10
Author(s):  
LeSheng Jin ◽  
Ronald R. Yager ◽  
Jana Špirková ◽  
Radko Mesiar ◽  
Daniel Paternain ◽  
...  
Keyword(s):  
Decision Makers ◽  
Input Vector ◽  
Weighted Averaging ◽  
Preference Aggregation ◽  
Uncertain Information ◽  
Interval Vector ◽  
Owa Operators ◽  
Ordered Weighted Averaging ◽  
Wide Range ◽  
Original Information

Basic Uncertain Information (BUI) as a newly introduced concept generalized a wide range of uncertain information. The well-known Ordered Weighted Averaging (OWA) operators can flexibly and effectively model bipolar preferences of decision makers over given real valued input vector. However, there are no extant methods for OWA operators to be carried out over given BUI vectors. Against this background, this study firstly discusses the interval transformation for BUI and elaborately explains the reasonability within it. Then, we propose the corresponding preference aggregations for BUI in two different decisional scenarios, the aggregation for BUI vector without original information influencing and the aggregation for BUI vector with original information influencing after interval transformation. For each decisional scenario, we also discuss two different orderings of preference aggregation, namely, interval-vector and vector-interval orderings, respectively. Hence, we will propose four different aggregation procedures of preference aggregation for BUI vector. Some illustrative examples are provided immediately after the corresponding aggregation procedures.

scholarly journals Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 830
Author(s):  
Yuan Feng ◽  
Baoan Ren ◽  
Chengyi Zeng ◽  
Yuyuan Yang ◽  
Hongfu Liu
Keyword(s):  
Real World ◽  
Similarity Index ◽  
Structural Similarity ◽  
Experimental Results ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Structural Similarity Index ◽  
Ordered Weighted Averaging ◽  
Long Time ◽  
Attack Strategy

Network disintegration has been an important research hotspot in complex networks for a long time. From the perspective of node attack, researchers have devoted to this field and carried out numerous works. In contrast, the research on edge attack strategy is insufficient. This paper comprehensively evaluates the disintegration effect of each structural similarity index when they are applied to the weighted-edge attacks model. Experimental results show that the edge attack strategy based on a single similarity index will appear limited stability and adaptability. Thus, motivated by obtaining a stable disintegration effect, this paper designs an edge attack strategy based on the ordered weighted averaging (OWA) operator. Through final experimental results, we found that the edge attack strategy proposed in this paper not only achieves a more stable disintegration effect on eight real-world networks, but also significantly improves the disintegration effect when applied on a single network in comparison with the original similarity index.

On the OWA Aggregation with Probabilistic Inputs

2015 ◽  
Vol 23 (Suppl. 1) ◽  
pp. 143-162 ◽  
Author(s):  
Ronald R. Yager
Keyword(s):  
Stochastic Dominance ◽  
Probability Distributions ◽  
Decision Problems ◽  
Weighted Averaging ◽  
Linear Ordering ◽  
Owa Operator ◽  
Dominance Relationship ◽  
Ordered Weighted Averaging ◽  
The Individual ◽  
The Relationship

We discuss the use of the ordered weighted averaging (OWA) operator in multi-criteria decision problems as a means of aggregating the individual criteria satisfactions. We emphasize the need for ordering the arguments, the criteria satisfactions, when using the OWA operator. We consider the situation where the criteria satisfactions have some uncertainty, are finite probability distributions and note the requirement of having to order probability distributions. We introduce the idea of using pairwise stochastic dominance to provide the necessary ordering relationship over the probability distributions. We note that while this approach is appropriate, it is often not possible, since the presence of a stochastic dominance relationship between all pairs of probability distributions is not always the case, the relationship is not complete. To circumvent this problem we introduce an approach called the probabilistic exceedance method (PEM), which allows us to provide a surrogate for the OWA aggregation of probability distributions that doesn't require a linear ordering over the probability distributions. We look at this in both the cases in which the criteria have equal and unequal importances.

EOWA AND EOWG OPERATORS FOR AGGREGATING LINGUISTIC LABELS BASED ON LINGUISTIC PREFERENCE RELATIONS

2004 ◽  
Vol 12 (06) ◽  
pp. 791-810 ◽  
Author(s):  
Z. S. XU
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Relation ◽  
Geometric Mean ◽  
Weighted Averaging ◽  
Preference Relations ◽  
Ordered Weighted Averaging ◽  
Linguistic Preference Relation ◽  
Linguistic Labels

In this paper, we define two types of linguistic preference relations (multiplicative linguistic preference relation and additive linguistic preference relation), and study some of their desirable properties. We introduce the extended geometric mean (EGM) operator, extended arithmetical averaging (EAA) operator, extended ordered weighted averaging (EOWA) operator and extended ordered weighted geometric (EOWG) operator. An approach based on the EGM and EOWG operators and multiplicative linguistic preference relations and an approach based on the EAA and EOWA operators and additive linguistic preference relations are proposed to ranking the alternatives in the group decision-making problems. Finally, we give a numerical example to illustrate the developed approaches.

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