1,721,021 research outputs found
From Undirected Structures to Directed Graphical Lasso Fuzzy Cognitive Maps using Ranking-based Approaches
No full textFuzzy cognitive maps (FCMs) have gained popularity within the scientific community due to their capabilities in modelling and decision making for complex problems. However, learning FCM models automatically from data without any expert knowledge and/or historical data remains a considerable challenge. For our research, we use the estimated weight matrix from the graphical lasso (glasso) method with the EBIC regulation technique. Particularly, the glasso is a technique originated from machine learning which is used to model a problem by learning the weight matrix directly from a dataset. Moreover, the relationships are expressed by conditional independence among two nodes after conditioning on all the other nodes of the graph. However, the challenging task in this study is the investigation of the suitable transformation of the weight matrix from a symmetric matrix to asymmetric in order to determine the directions of the edges among the concepts and construct the glassoFCM model. For this reason, statistical comparisons are applied to examine if there are significant differences in the value of the output concept when the input concepts are rearranged according to four different cases. The whole approach was implemented in a business intelligence problem of evaluating the willingness of the employees to work in Belgian companies
Multi criteria methods used for assessing for companies' attractiveness
No full textMany researchers have studied about attractiveness of a company in business domain but survey after survey, reveals that there is always room for improvement. In this paper, human reasoning extraction is investigated to evaluate the attractiveness of each company resident in Belgium. The term attractiveness refers to the case that a company X pays attention only to customers’ preference to boosting employee satisfaction, help the company retain personnel and attract new employees. This leads to brand name improvement which allows the company to increase the sales of their products or services, remain competitive in the market and increase the employee productivity. These effects can be achieved if the company will focus and improve the factors that participants considered more important. Three Multi-Criteria Decision Making methods: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytical Hierarchy Process (AHP) and Weighted Sum Model (WSM) were deployed to identify and order the most important factors that influence the company competitiveness based in customer satisfaction. The challenging part of this study is the exploitation of "pure" knowledge from participants, the comparison of results and finally, the aggregation of all accomplished evaluations, without expert knowledge and consequently without weights and criteria. To accomplish this goal, we customized TOPSIS and AHP methods to deal with participants' consensus, without using common voting methods but methods based on MCDM.
14.585 questionnaires were gathered from people in Belgium and 349 companies, which were resident in Belgium, participated in this research. It is important to note that the respondents didn’t have any information about the name of each company. The most significant factors were selected from respondents on the assumption that they wished to choose a company X to be employed in. The questionnaire was divided into two parts, Data Set 1 (DS1) and Data Set 2 (DS2). In DS1, participants gave their preference value only to five of the seventeen factors that consider more important. In DS2, participants had not any limitation for the factors' choice. Each participant had to split the amount of one thousand points to factors that they considered most important by giving more points to the most significant factor. Before ordering the factors, the first procedure was to clean the data in order to achieve the best results. As mentioned before, there is no knowledge about criteria, which are important to determine the ranking, especially in TOPSIS and AHP method. In this paper, each horizontal row of the decision matrix is allocated to one factor and each vertical column to one participant’s opinion. All participants had equal importance so no weights were required.
The results have shown that rankings of seventeen factors (of thousands of participants’ opinions which were distributed in fifteen sectors), were similar in three methods. The five top factors that each company is interested to improve were at the top of the list. Since TOPSIS and AHP method was proved effective in our problem to rank properly the factors, we applied once again TOPSIS and AHP method to aggregate the people consensus in the final classification. In these cases, seventeen factors were used as an input (rows) in the decision matrix and fifteen sectors were inserted as columns and applied in DS1 and DS2. Fifteen rankings (one for each sector), that have been ordered with TOPSIS method, were aggregated into a final order, using TOPSIS method and other fifteen rankings that have been ordered with TOPSIS method, were aggregated into a single one, using AHP method.
As noted in the final standings the top five factors or the most significant factors, are common in both different data sets. The purpose of the final ranking was to aggregate the common opinion (or else the consensus) and reflect the significant factors that need to improve when the company X cares about the attractiveness or wants to enhance the competitive advantage. The ranking of top 5 factors show that people prefer long term job security and competitive salary package more than offering of interesting jobs, financially sound and pleasant working environment. It also showed that people in Belgium prefer stability (long-term security) instead of jobs financially sound. However, this outcome is not surprising and it could be a direct result of the economic crisis
Open source tool in R language to estimate the inference of the Fuzzy Cognitive Map in environmental decision making
No full textFuzzy cognitive maps (FCMs) have gained popularity within the scientific community due to
their capabilities in modelling and decision making for complex problems. However, despite the large
number of papers presenting advances in mathematical formulation and applications of FCMs, along
with some recent tools for this soft computing technique, there is a lack of open source tools with
sufficient flexibility for modelling and inference in diverse application domains. Filling this gap, this paper
presents an open source package in R programming language, called the ‘fcm’ package, which is able
to do scenario analyses and to examine and estimate the inference of FCM, using the fcm.infer function.
Six different inference rules (kosko, modified-kosko, rescale rule, and the clamped versions of these
rules) and four threshold functions (bivalent, trivalent, sigmoid and hyperbolic tangent) are provided.
This open-source package is available in CRAN and is relatively easy to use, even for less experienced
users. It thus provides the opportunity for researchers in different fields to analyse their weighted
matrices as FCM. This paper shows examples and visualizations to demonstrate the proposed open
source FCM package for environmental modelling and decision making
Retrieving Sparser Fuzzy Cognitive Maps Directly from Categorical Ordinal Dataset using the Graphical Lasso Models and the MAX-threshold Algorithm
No full textLearning FCM models from data without any a priori knowledge and expert intervention remains a considerable problem. This research study utilizes a fully data-based learning method (the glassoFCM) for automatic design of Fuzzy Cognitive Maps (FCM) using large ordinal dataset based on the efficient capabilities of graphical lasso (glasso) models. Therefore, glasso represents its structure as a sparser graph, while maintaining a high likelihood, by producing an adjacent weighted matrix, where relationships are expressed by conditional independences. By minimizing the negative log-likelihood indicates that the model fits better to the data under the assumption that the observed data are the most likely data. The principle questioning is which of the observed concepts is the appropriate to trigger the remaining concepts in the map in order to create the glassoFCMs and obtain reasonable results. The answer derives from the FCM structure analysis based on the strength centrality indices. Moreover, the MAX-threshold algorithm based on the FCM scenario analysis is proposed in order to prune edges and retrieve sparser graphs. This algorithm shrinks the meaningless weights of the FCM, without affecting significantly the outcomes in scenario analysis. The whole approach was implemented in a business intelligence problem of evaluating the attractiveness of Belgian companies
Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management
No full textA social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence
A Modified Fuzzy TOPSIS Method Aggregating 8.921 Partial Rankings For Companies’ Attractiveness
No full textReal-life environments are inadequate to be modelled by crisp values, since hu-man reasoning is often uncertain and ambiguous. Therefore, the aggregation of fuzzy concept of decision makers is represented sufficiently with fuzzy (impre-cise) data. The purpose of this paper is the development of a powerful and useful method based on fuzzy TOPSIS which is able to aggregate judgements of 8.921 decision makers in a real fuzzy environment. The main goal of the proposed mod-ified fuzzy TOPSIS method is the efficiently ordering of a big volume of partial ranking lists related with 17 factors which are associated with the job satisfaction in fifteen different sectors. The results are very promising to continue our re-search to this direction and make further investigations.Hasselt Universit
A computational tool for simulation and learning of Fuzzy Cognitive Maps
No full textDuring the last decade Fuzzy Cognitive Maps (FCM) have become a useful tool for solving unstructured problems. In a few words they could be defined as Recurrent Neural Networks for simulating complex systems, where neurons denote concepts, objects or entities of the investigated system. Normally FCM are entirely designed using the best knowledge of a group of experts in a given domain, so frequently learning algorithms for tuning the model parameters are required. Despite the theoretical advances in such fields, the lack of a suitable computational framework for handling FCM-based systems is still an open problem. This paper introduces a novel tool for designing and simulating FCM which gathers several learning algorithms for adjusting the introduced parameters. More specifically, the framework includes supervised and unsupervised learning algorithms for computing the causal weights, algorithms for optimizing the network topology in large FCM (without losing significant information) and also methods for improving the global convergence on continuous FCM. It should be stated that these algorithms are oriented to prediction tasks, but they could be easily extended to other fields
Aggregation of Partial Rankings - An Approach Based on the Kemeny Ranking Problem
No full textAggregating the preference of multiple experts is a very old problem which remains without an absolute solution. This assertion is supported by the Arrow's theorem: there is no aggregation method that simultaneously satisfies three fairness criteria (non-dictatorship, independence of irrelevant alternatives and Pareto efficiency). However, it is possible to find a solution having minimal distance to the consensus, although it involves a NP-hard problem even for only a few experts. This paper presents a model based on Ant Colony Optimization for facing this problem when input data are incomplete. It means that our model should build a complete ordering from partial rankings. Besides, we introduce a measure to determine the distance between items. It provides a more complete picture of the aggregated solution. In order to illustrate our contributions we use a real problem concerning Employer Branding issues in Belgium
Experimental Comparative Analysis of Centralized vs. Decentralized Coordination of Aerial–Ground Robotic Teams for Agricultural Operations
Full text linkReliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication frameworks: (a) MAVLink (decentralized) and (b) Farm Management Information System (FMIS) (centralized). Field experiments were conducted in both empty field and orchard environments, using a rotary UAV for worker detection and a UGV responding to intent signaled through color-coded hats. Across 120 trials, the system performance was assessed in terms of communication reliability, latency, energy consumption, and responsiveness. FMIS consistently demonstrated higher message delivery success rates (97% in both environments) than MAVLink (83% in the empty field and 70% in the orchard). However, it resulted in higher UGV resource usage. Conversely, MAVLink achieved reduced UGV power draw and lower latency, but it was more affected by obstructed settings and also resulted in increased UAV battery consumption. In conclusion, MAVLink is suitable for time-sensitive operations that require rapid feedback, while FMIS is better suited for tasks that demand reliable communication in complex agricultural environments. Consequently, the selection between MAVLink and FMIS should be guided by the specific mission goals and environmental conditions
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