Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369114
Y. Donoso, Carolina Alvarado, Alfredo J. Perez, Ivan Herazo
This paper shows the solution of a multiobjective scheme for multicast transmissions in MPLS networks with a GMLS optical backbone using evolutive algorithms. It has not been showed models that optimize one or more parameters integrating these two types of networks. Because the proposed scheme is a NP-hard problem, an algorithm has been developed to solve the problem on polynomial time. The main contributions of this paper are the proposed mathematical model and the algorithm to solve it
{"title":"A Multi-Objective Solution Applying MOEA in Optical Networks","authors":"Y. Donoso, Carolina Alvarado, Alfredo J. Perez, Ivan Herazo","doi":"10.1109/MCDM.2007.369114","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369114","url":null,"abstract":"This paper shows the solution of a multiobjective scheme for multicast transmissions in MPLS networks with a GMLS optical backbone using evolutive algorithms. It has not been showed models that optimize one or more parameters integrating these two types of networks. Because the proposed scheme is a NP-hard problem, an algorithm has been developed to solve the problem on polynomial time. The main contributions of this paper are the proposed mathematical model and the algorithm to solve it","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115926468","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369408
P. Bonissone
Summary form only given. The goal of the First IEEE Symposium of Computational Intelligence in Multicriteria Decision Making (MCDM 2007) is to provide a common forum for three scientific communities that have addressed different aspects of the MCDM problem and provided complementary approaches to its solution. The first approach is the search process over the space of possible solutions. We must perform efficient searches in multi- (or sometimes many-) dimensional spaces to identify the non-dominated solutions that compose the Pareto set. This search is driven by the solution evaluations, which might be probabilistic, stochastic, or imprecise, rather than deterministic. The second approach is the preference tradeoff process. We need to elicit, represent, evaluate, and aggregate the decision-maker's preferences to select a single solution (or a small subset of solutions) from the Pareto set. These preferences may be ill defined, and state or time-dependent rather than constant values. The aggregation mechanism may be as simple as a linear combination or as complex as a knowledge-driven model. The third approach is the interactive visualization process, which enables progressive decisions. We often want to embed the decision-maker in the solution refinement and selection loop. To this end, we need to show the impacts that intermediate tradeoffs in one sub-space could have in the other ones, while allowing him/her to retract or modify any intermediate steps to strike appropriate tradeoff balances. Given this perspective, we believe that MCDM resides in the intersections of these approaches
{"title":"Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff, and Interaction Visualization Processes","authors":"P. Bonissone","doi":"10.1109/MCDM.2007.369408","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369408","url":null,"abstract":"Summary form only given. The goal of the First IEEE Symposium of Computational Intelligence in Multicriteria Decision Making (MCDM 2007) is to provide a common forum for three scientific communities that have addressed different aspects of the MCDM problem and provided complementary approaches to its solution. The first approach is the search process over the space of possible solutions. We must perform efficient searches in multi- (or sometimes many-) dimensional spaces to identify the non-dominated solutions that compose the Pareto set. This search is driven by the solution evaluations, which might be probabilistic, stochastic, or imprecise, rather than deterministic. The second approach is the preference tradeoff process. We need to elicit, represent, evaluate, and aggregate the decision-maker's preferences to select a single solution (or a small subset of solutions) from the Pareto set. These preferences may be ill defined, and state or time-dependent rather than constant values. The aggregation mechanism may be as simple as a linear combination or as complex as a knowledge-driven model. The third approach is the interactive visualization process, which enables progressive decisions. We often want to embed the decision-maker in the solution refinement and selection loop. To this end, we need to show the impacts that intermediate tradeoffs in one sub-space could have in the other ones, while allowing him/her to retract or modify any intermediate steps to strike appropriate tradeoff balances. Given this perspective, we believe that MCDM resides in the intersections of these approaches","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131079302","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369433
T. Yoshikawa, Daisuke Yamashiro, T. Furuhashi
The rapid progresses of computers introduce evolutionary computations to next step, which is the demand for the variety of Pareto solutions in multi-objective optimization problems. We can calculate a large amount of Pareto solutions in a short time. However, it is difficult to use the acquired Pareto solutions effectively, because the Pareto solutions have multi-dimension of fitness values. This study tries to develop "mining of solutions" technique with visualization. This paper proposes a visualizing method for Pareto solutions which have multi-objective fitness values. The proposed method enables us to grasp the distributed structure of Pareto solutions and clarify the relationship among multi-objective fitness values. This paper shows that the visualized data enables us to interpret the characteristics of Pareto solutions through experimental result
{"title":"A Proposal of Visualization of Multi-Objective Pareto Solutions -Development of Mining Technique for Solutions-","authors":"T. Yoshikawa, Daisuke Yamashiro, T. Furuhashi","doi":"10.1109/MCDM.2007.369433","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369433","url":null,"abstract":"The rapid progresses of computers introduce evolutionary computations to next step, which is the demand for the variety of Pareto solutions in multi-objective optimization problems. We can calculate a large amount of Pareto solutions in a short time. However, it is difficult to use the acquired Pareto solutions effectively, because the Pareto solutions have multi-dimension of fitness values. This study tries to develop \"mining of solutions\" technique with visualization. This paper proposes a visualizing method for Pareto solutions which have multi-objective fitness values. The proposed method enables us to grasp the distributed structure of Pareto solutions and clarify the relationship among multi-objective fitness values. This paper shows that the visualized data enables us to interpret the characteristics of Pareto solutions through experimental result","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123716431","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369434
M. Geiger, W. Wenger, W. Habenicht
The article presents an interactive multi-criteria approach for the resolution of rich vehicle routing problems. A flexible framework was built to be able to deal with various components of general vehicle routing problems, e.g. the consideration of multiple objectives or different types of specific complex side constraints such as time windows, multiple depots or heterogeneous fleets. In the framework, a local search approach on the basis of variable neighborhood search (VNS) constructs and improves solutions in real time. The decision maker is actively involved into the resolution process as the system allows the interactive articulation of preference information, influencing the global utility function that guides the search. Results of test runs on multiple depot multi-objective vehicle routing problems with time windows are reported, simulating different types of decision maker behaviors
{"title":"Interactive Utility Maximization in Multi-Objective Vehicle Routing Problems: A \"Decision Maker in the Loop\"-Approach","authors":"M. Geiger, W. Wenger, W. Habenicht","doi":"10.1109/MCDM.2007.369434","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369434","url":null,"abstract":"The article presents an interactive multi-criteria approach for the resolution of rich vehicle routing problems. A flexible framework was built to be able to deal with various components of general vehicle routing problems, e.g. the consideration of multiple objectives or different types of specific complex side constraints such as time windows, multiple depots or heterogeneous fleets. In the framework, a local search approach on the basis of variable neighborhood search (VNS) constructs and improves solutions in real time. The decision maker is actively involved into the resolution process as the system allows the interactive articulation of preference information, influencing the global utility function that guides the search. Results of test runs on multiple depot multi-objective vehicle routing problems with time windows are reported, simulating different types of decision maker behaviors","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114997343","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369440
R. Yager
Summary form only given. Because of its ability to provide a bridge between linguistic expression and mathematical modeling fuzzy sets technology provides an idea framework for the construction of multi-criteria decision functions. In this talk we shall describe a number of aggregation operators associated with fuzzy set theory and see how they can be used to formulate multi-criteria decision functions. Particular attention was paid to formulating multi-criteria functions from linguistically specified user requirements.
{"title":"Fuzzy Methods for Constructing Multi-Criteria Decision Functions","authors":"R. Yager","doi":"10.1109/MCDM.2007.369440","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369440","url":null,"abstract":"Summary form only given. Because of its ability to provide a bridge between linguistic expression and mathematical modeling fuzzy sets technology provides an idea framework for the construction of multi-criteria decision functions. In this talk we shall describe a number of aggregation operators associated with fuzzy set theory and see how they can be used to formulate multi-criteria decision functions. Particular attention was paid to formulating multi-criteria functions from linguistically specified user requirements.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904823","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369423
A. Reynolds, B. Iglesia
The most successful multi-objective metaheuristics, such as NSGA II and SPEA 2, usually apply a form of elitism in the search. However, there are multi-objective problems where this approach leads to a major loss of population diversity early in the search. In earlier work, the authors applied a multi-objective metaheuristic to the problem of rule induction for predictive classification, minimizing rule complexity and misclassification costs. While high quality results were obtained, this problem was found to suffer from such a loss of diversity. This paper describes the use of both linear combinations of objectives and modified dominance relations to control population diversity, producing higher quality results in shorter run times
{"title":"Managing Population Diversity Through the Use of Weighted Objectives and Modified Dominance: An Example from Data Mining","authors":"A. Reynolds, B. Iglesia","doi":"10.1109/MCDM.2007.369423","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369423","url":null,"abstract":"The most successful multi-objective metaheuristics, such as NSGA II and SPEA 2, usually apply a form of elitism in the search. However, there are multi-objective problems where this approach leads to a major loss of population diversity early in the search. In earlier work, the authors applied a multi-objective metaheuristic to the problem of rule induction for predictive classification, minimizing rule complexity and misclassification costs. While high quality results were obtained, this problem was found to suffer from such a loss of diversity. This paper describes the use of both linear combinations of objectives and modified dominance relations to control population diversity, producing higher quality results in shorter run times","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861110","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369426
N. Kondo, T. Hatanaka, K. Uosaki
In this paper, nonlinear dynamic system identification by using multiobjectively selected RBF network is considered. RBF networks are widely used as a model structure for nonlinear systems. The determination of its structure that is the number of basis functions is prior important step in system identification, and the tradeoff between model complexity and accuracy exists in this problem. By using multiobjective evolutionary algorithms, the candidates of the RBF network structure are obtained in the sense of Pareto optimality. We discuss an application to system identification by using such RBF networks having Pareto optimal structures. Some numerical simulations for nonlinear dynamic systems are carried out to show the applicability of the proposed approach.
{"title":"Nonlinear Dynamic System Identification Based on Multiobjectively Selected RBF Networks","authors":"N. Kondo, T. Hatanaka, K. Uosaki","doi":"10.1109/MCDM.2007.369426","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369426","url":null,"abstract":"In this paper, nonlinear dynamic system identification by using multiobjectively selected RBF network is considered. RBF networks are widely used as a model structure for nonlinear systems. The determination of its structure that is the number of basis functions is prior important step in system identification, and the tradeoff between model complexity and accuracy exists in this problem. By using multiobjective evolutionary algorithms, the candidates of the RBF network structure are obtained in the sense of Pareto optimality. We discuss an application to system identification by using such RBF networks having Pareto optimal structures. Some numerical simulations for nonlinear dynamic systems are carried out to show the applicability of the proposed approach.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128063370","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369108
H. Nakayama, Y. Yun
Since Pareto optimal solutions in multi-objective optimization are not unique but makes a set, decision maker (DM) needs to select one of them as a final decision. In this event, DM tries to find a solution making a well balance among multiple objectives. Aspiration level methods support DM to do this in an interactive way, and are very simple, easy and intuitive for DMs. Their effectiveness has been observed through various fields of practical problems. One of authors proposed the satisficing trade-off method early in '80s, and applied it to several kinds of practical problems. On the other hand, in many engineering design problems, the explicit form of objective function can not be given in terms of design variables. Given the value of design variables, under this circumstance, the value of objective function is obtained by some simulation analysis or experiments. Usually, these analyses are computationary expensive. In order to make the number of analyses as few as possible, several methods for sequential approximate optimization which make optimization in parallel with model prediction has been proposed. In this paper, we form a coalition between aspiration level methods and sequential approximate optimization methods in order to get a final solution for multi-objective engineering problems in a reasonable number of analyses. In particular, we apply mu-nu-SVM which was developed by the authors on the basis of goal programming. The effectiveness of the proposed method was shown through some numerical experiments.
{"title":"Combining Aspiration Level Methods in Multi-objective Programming and Sequential Approximate Optimization using Computational Intelligence","authors":"H. Nakayama, Y. Yun","doi":"10.1109/MCDM.2007.369108","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369108","url":null,"abstract":"Since Pareto optimal solutions in multi-objective optimization are not unique but makes a set, decision maker (DM) needs to select one of them as a final decision. In this event, DM tries to find a solution making a well balance among multiple objectives. Aspiration level methods support DM to do this in an interactive way, and are very simple, easy and intuitive for DMs. Their effectiveness has been observed through various fields of practical problems. One of authors proposed the satisficing trade-off method early in '80s, and applied it to several kinds of practical problems. On the other hand, in many engineering design problems, the explicit form of objective function can not be given in terms of design variables. Given the value of design variables, under this circumstance, the value of objective function is obtained by some simulation analysis or experiments. Usually, these analyses are computationary expensive. In order to make the number of analyses as few as possible, several methods for sequential approximate optimization which make optimization in parallel with model prediction has been proposed. In this paper, we form a coalition between aspiration level methods and sequential approximate optimization methods in order to get a final solution for multi-objective engineering problems in a reasonable number of analyses. In particular, we apply mu-nu-SVM which was developed by the authors on the basis of goal programming. The effectiveness of the proposed method was shown through some numerical experiments.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132558602","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369102
M. Zarghami, R. Ardakanian, F. Szidarovszky
The successful design and application of the ordered weighted averaging (OWA) method as a decision making tool depends on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability methods which give different behavior patterns for OWA. These methods will be compared by using sensitivity analysis on the outputs of OWA with respect to the optimism degree of the decision maker. The theoretical results are illustrated in a water resources management problem. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree and therefore it has better computation efficiency. A simulation study is also reported in this paper, where the dependence of the optimal decision on the uncertainty level is examined. Also based on obtained sensitivity measure, a new combined measure of goodness has been defined to have more reliability in obtaining optimal solutions
{"title":"Obtaining robust decisions under uncertainty by sensitivity analysis on OWA operator","authors":"M. Zarghami, R. Ardakanian, F. Szidarovszky","doi":"10.1109/MCDM.2007.369102","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369102","url":null,"abstract":"The successful design and application of the ordered weighted averaging (OWA) method as a decision making tool depends on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability methods which give different behavior patterns for OWA. These methods will be compared by using sensitivity analysis on the outputs of OWA with respect to the optimism degree of the decision maker. The theoretical results are illustrated in a water resources management problem. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree and therefore it has better computation efficiency. A simulation study is also reported in this paper, where the dependence of the optimal decision on the uncertainty level is examined. Also based on obtained sensitivity measure, a new combined measure of goodness has been defined to have more reliability in obtaining optimal solutions","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123932054","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}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369449
Ching-I Cheng, D. Liu
The project, Dressing Consultant, aims to provide a system which functions as a personal wearing advisor to help general users choose a correct clothing for occasions. ALCOVE (attention learning covering network) neural network model is used to train the matchmaker as a fashion editor. In addition, image processing techniques are employed at pre-processing stage to obtain the essential data of garments and to build a digital wardrobe for individuals. On the occasions when user has trouble finding an outfit for a special event, what user could do is to make a decision of the style of apparel to the system and let the system go through piece of garments in the digital wardrobe, and the matchmaker will then find several matched pairs. Eventually, the most similarly suitable and matched garments pair is shown in 3D show room. This paper focuses on making decision of correct clothing according to those classifying and matching rules extracted from fashion industry
{"title":"A Decision Making Framework for Dressing Consultant","authors":"Ching-I Cheng, D. Liu","doi":"10.1109/MCDM.2007.369449","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369449","url":null,"abstract":"The project, Dressing Consultant, aims to provide a system which functions as a personal wearing advisor to help general users choose a correct clothing for occasions. ALCOVE (attention learning covering network) neural network model is used to train the matchmaker as a fashion editor. In addition, image processing techniques are employed at pre-processing stage to obtain the essential data of garments and to build a digital wardrobe for individuals. On the occasions when user has trouble finding an outfit for a special event, what user could do is to make a decision of the style of apparel to the system and let the system go through piece of garments in the digital wardrobe, and the matchmaker will then find several matched pairs. Eventually, the most similarly suitable and matched garments pair is shown in 3D show room. This paper focuses on making decision of correct clothing according to those classifying and matching rules extracted from fashion industry","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122475763","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}
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