Dimensionality reduction of inputs for a Fuzzy Cognitive Map for obesity problem

Selvin, Nancy Nadar; Srinivasaraghavan, Anuradha

doi:10.1109/inventive.2016.7830187

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…An enormous amount of data and a big number of concepts may complicate analysis and decision-making. In this direction, various modifications to standard methods have been applied aiming to simplify FCM models by reducing the number of concepts [14][15][16] and the connections between them [17,18]. These approaches allow obtaining a balance between data accuracy and model readability.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

2020

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Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

2020

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“…The obtained results confirmed that this approach for simplifying complex FCM models allows to achieve a reasonable balance between complexity and modeling accuracy. Selvin and Srinivasaraghavan proposed an application of the feature selection techniques to reduce the number of the input concepts of fuzzy cognitive map [21]. The feature selection methods were performed based on the significance of each concept to the output concept.…”

Section: Introductionmentioning

confidence: 99%

Concepts Selection in Fuzzy Cognitive Map using Evolutionary Learning Algorithm based on Graph Theory Metrics

Poczęta

Kubuś²,

Yastrebov

2017

Annals of Computer Science and Information Systems

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Abstract-Fuzzy cognitive map (FCM) allows to discover knowledge in the form of concepts significant for the analyzed problem and causal connections between them. The FCM model can be developed by experts or using learning algorithms and available data. The main aspect of building of the FCM model is concepts selection. It is usually based on the expert knowledge. The aim of this paper is to develop and analyze a new evolutionary algorithm for selection of key concepts and determining the weights of the connections between them on the basis of available data. The proposed approach allows to reduce concepts during learning process based on metrics from the area of graph theory: significance of each node and total influence of the concept. A simulation analysis of the developed algorithm was done with the use of real-life data.

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