Multiple Criteria Decision Making最新文献_第4页

Impact of the COVID-19 pandemic on the expression of emotions in social media COVID-19大流行对社交媒体情绪表达的影响

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.22367/mcdm.2020.15.02

Debabrata Ghosh

{"title":"Impact of the COVID-19 pandemic on the expression of emotions in social media","authors":"Debabrata Ghosh","doi":"10.22367/mcdm.2020.15.02","DOIUrl":"https://doi.org/10.22367/mcdm.2020.15.02","url":null,"abstract":"In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. Keywords: classification, COVID-19, emotion, emotion analysis, Naïve Bayes, Pandemic, Random Forest, SVM.","PeriodicalId":259350,"journal":{"name":"Multiple Criteria Decision Making","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122680014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Perspectives on Multi-criteria Decision Analysis and Life-Cycle Assessment 多准则决策分析与生命周期评价展望

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-11482-4_12

L. Dias, F. Freire, Jutta Geldermann

引用次数: 11

Analysis of Facts: Issues and Questions Answered in MCDM Practice 事实分析:MCDM实践中的问题与解答

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-82347-4_3

N. Munier

引用次数: 0

Linear Programming and the SIMUS Method 线性规划与SIMUS方法

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-82347-4_2

N. Munier

引用次数: 0

Spare parts quantity problem under uncertainty - the case of entirely new devices with short life cycle 不确定条件下的备件数量问题——生命周期短的全新设备的情况

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.22367/mcdm.2019.14.01

Helena Gaspars-Wieloch

{"title":"Spare parts quantity problem under uncertainty - the case of entirely new devices with short life cycle","authors":"Helena Gaspars-Wieloch","doi":"10.22367/mcdm.2019.14.01","DOIUrl":"https://doi.org/10.22367/mcdm.2019.14.01","url":null,"abstract":"The paper presents a new scenario-based decision rule for the spare parts quantity problem (SPQP) under uncertainty with unknown objective probabilities. The goal of SPQP is to ensure the right number of extra parts at the right place at the right time. In the literature, SPQP is usually regarded as a stochastic problem since the demand for extra parts is treated as a random variable with a known distribution. The optimal stock quantity minimizes the expected loss resulting from buying a given number of parts before potential failures. The novel approach is designed for the purchase of non-repairable spare parts for entirely new seasonal devices, where the estimation of frequencies is complicated because there are no historical data about previous failures. Additionally, the decision maker’s knowledge is limited due to the nature of the problem. The new procedure is a three-criteria method. It is based on the Hurwicz and Bayes decision rules and supported with a forecasting stage enabling one to set the scenario with the greatest subjective chance of occurrence. The method takes into account the decision maker’s attitude towards risk and the asymmetry of losses connected with particular stock quantities. We assume that the future unit purchase cost of a service part bought after the breakdown is also uncertain and given as an interval parameter. The approach is designed for short life cycle machines.","PeriodicalId":259350,"journal":{"name":"Multiple Criteria Decision Making","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126688497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A PROMETHEE II-BELIEFapproach for multi-criteria decision-making problems with incomplete information 信息不完全多准则决策问题的PROMETHEE - belief方法

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.22367/mcdm.2021.16.04

H. Frikha, A. Frikha

{"title":"A PROMETHEE II-BELIEFapproach for multi-criteria decision-making problems with incomplete information","authors":"H. Frikha, A. Frikha","doi":"10.22367/mcdm.2021.16.04","DOIUrl":"https://doi.org/10.22367/mcdm.2021.16.04","url":null,"abstract":"Multi-criteria decision aid methods consider decision problems in which many alternatives are evaluated on several criteria. These methods are used to deal with perfect information. However, in practice, it is obvious that this information requirement is too strict. In fact, the imperfect data provided by more or less reliable decision makers usually affect decision results, since any decision is closely linked to the quality and availability of information. In this paper, a PROMETHEE II-BELIEF approach is proposed to help multi-criteria decisions based on incomplete information. This approach solves problems with incomplete decision matrix and unknown weights within PROMETHEE II method. On the basis of belief function theory, our approach first determines the distributions of belief masses based on PROMETHEE II’s net flows, and then calculates weights. Subsequently, it aggregates the distribution masses associated with each criterion using Murphy’s modified combination rule in order to infer a global belief structure. The final alternative ranking is obtained via pignistic probability transformation. A case study of a real-world application concerning the location of a treatment center of waste from healthcare activities with infectious risk in the center of Tunisia is studied to illustrate the detailed process of the PROMETHEE II-BELIEF approach. Keywords: multiple criteria aid, incomplete information, PROMETHEE II method, belief function theory.","PeriodicalId":259350,"journal":{"name":"Multiple Criteria Decision Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134629207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fuzzy multicriteria approach for the trading systems on the Forex Market 外汇市场交易系统的模糊多准则方法

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.22367/mcdm.2019.14.02

Przemysław Juszczuk, L. Kruś

引用次数: 1

Analytic Hierarchy Process and Its Extensions 层次分析法及其扩展

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-11482-4_2

A. Ishizaka

引用次数: 19

Optimization and Multicriteria Evaluation of District Heat Production and Storage 区域产热蓄热优化及多指标评价

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-11482-4_15

R. Lahdelma, G. Kayo, Elnaz Abdollahi, P. Salminen

引用次数: 3

A new approach for criteria weight elicitation of the ARAS-H method ARAS-H法标准权重推导的新方法

Multiple Criteria Decision Making Pub Date : 1900-01-01 DOI: 10.22367/mcdm.2021.16.05

Maroua Ghram, H. Frikha

{"title":"A new approach for criteria weight elicitation of the ARAS-H method","authors":"Maroua Ghram, H. Frikha","doi":"10.22367/mcdm.2021.16.05","DOIUrl":"https://doi.org/10.22367/mcdm.2021.16.05","url":null,"abstract":"Criteria weight inference is a crucial step for most of multi-criteria methods. However, criteria weights are often determined directly by the decision-maker (DM) which makes the results unreliable. Therefore, to overcome the imprecise weighting, we suggest the use of the preference programming technique. Instead of obtaining criteria weights directly from the DM, we infer them in a more objective manner to avoid the subjectivity and the unreliability of the results. Our aim is to elicit the ARAS-H criteria weights at each level of the hierarchy tree via mathematical programming, taking into account the DM’s preferences. To put it differently, starting from preference information provided by the DM, we proceed to model our constraints. The ARAS-H method is an extension of the classical ARAS method for the case of hierarchically structured criteria. We adopt a bottom-up approach in order to elicit ARAS-H criteria weights, that is, we start by determining the elementary criteria weights (i.e. the criteria at the lowest level of the hierarchy tree). The solution of the linear programs is obtained using LINGO software. The main contribution of our criteria weight elicitation procedure is in overcoming imprecise weighting without excluding the DM from the decision making process. Keywords: Multiple Criteria Decision Aiding, preference disaggregation, ARAS-H, criteria weights, mathematical programming.","PeriodicalId":259350,"journal":{"name":"Multiple Criteria Decision Making","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130376842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0