The Experts below are selected from a list of 42768 Experts worldwide ranked by ideXlab platform
Stefano Cagnoni - One of the best experts on this subject based on the ideXlab platform.
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WIVACE - A Relevance Index-Based Method for Improved Detection of Malicious Users in Social Networks
Communications in Computer and Information Science, 2020Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Paolo Fornacciari, Michele Tomaiuolo, Stefano CagnoniAbstract:The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
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a Relevance Index based method for improved detection of malicious users in social networks
Workshop Artificial Life and Evolutionary Computation, 2019Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Paolo Fornacciari, Michele Tomaiuolo, Stefano CagnoniAbstract:The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
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From complex system analysis to pattern recognition: experimental assessment of an unsupervised feature extraction method based on the Relevance Index metrics
Computation, 2019Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Stefano CagnoniAbstract:The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.
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WIVACE - An improved Relevance Index method to search important structures in complex systems
Communications in Computer and Information Science, 2019Co-Authors: Laura Sani, Alberto Bononi, Riccardo Pecori, Michele Amoretti, Monica Mordonini, Andrea Roli, Marco Villani, Stefano Cagnoni, Roberto SerraAbstract:We present an improvement of a method that aims at detecting important dynamical structures in complex systems, by identifying subsets of elements that show tight and coordinated interactions among themselves, while interplaying much more loosely with the rest of the system. Such subsets are estimated by means of a Relevance Index (RI), which is normalized with respect to a homogeneous system, usually described by independent Gaussian variables, as a reference. The strategy presented herein improves the way the homogeneous system is conceived from a theoretical viewpoint. Firstly, we consider the system components as dependent and with equal pairwise correlations, which implies a non-diagonal correlation matrix of the homogeneous system. Then, we generate the components of the homogeneous system according to a multivariate Bernoulli distribution, by exploiting the NORTA method, which is able to create samples of a desired random vector, given its marginal distributions and its correlation matrix. The proposed improvement on the RI method has been applied to three different case studies, obtaining better results compared with the traditional method based on the homogeneous system with independent Gaussian variables.
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An iterative information-theoretic approach to the detection of structures in complex systems
Complexity, 2018Co-Authors: Marco Villani, Laura Sani, Riccardo Pecori, Michele Amoretti, Monica Mordonini, Andrea Roli, Roberto Serra, Stefano CagnoniAbstract:Systems that exhibit complex behaviours often contain inherent dynamical structures which evolve over time in a coordinated way. In this paper, we present a methodology based on the Relevance Index method aimed at revealing the dynamical structures hidden in complex systems. The method iterates two basic steps: detection of relevant variable sets based on the computation of the Relevance Index, and application of a sieving algorithm, which refines the results. This approach is able to highlight the organization of a complex system into sets of variables, which interact with one another at different hierarchical levels, detected, in turn, in the different iterations of the sieve. The method can be applied directly to systems composed of a small number of variables, whereas it requires the help of a custom metaheuristic in case of systems with larger dimensions. We have evaluated the potential of the method by applying it to three case studies: synthetic data generated by a nonlinear stochastic dynamical system, a small-sized and well-known system modelling a catalytic reaction, and a larger one, which describes the interactions within a social community, that requires the use of the metaheuristic. The experiments we made to validate the method produced interesting results, effectively uncovering hidden details of the systems to which it was applied.
Choon Seon Park - One of the best experts on this subject based on the ideXlab platform.
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Imbalance in Cardiovascular Surgery Medical Service Use Between Regions.
The Korean journal of thoracic and cardiovascular surgery, 2016Co-Authors: Myunghwa Kim, Seok-jun Yoon, Ji Suk Choi, Myo Jeong Kim, Sung Bo Sim, Kun Sei Lee, Hyun Keun Chee, Nam Hee Park, Choon Seon ParkAbstract:This study uses the Relevance Index to understand the condition of regional medical service use for cardiovascular surgery and to identify the medical service use imbalance between regions. This study calculated the Relevance Index of 16 metropolitan cities and provinces using resident registration address data from the Ministry of Government Administration and Home Affairs and the 2010-2014 health insurance, medical care assistance, and medical benefits claims data from the Health Insurance Review and Assessment Service. We identified developments over the 5-year time period and analyzed the level of regional imbalance regarding cardiovascular surgery through the relative comparison of Relevance Indexes between cardiovascular and other types of surgery. The Relevance Index was high in large cities such as Seoul, Daegu, and Gwangju, but low in regions that were geographically far from the capital area, such as the Gangwon and Jeju areas. Relevance Indexes also fell as the years passed. Cardiovascular surgery has a relatively low Relevance Index compared to key types of surgery of other fields, such as neurosurgery and colorectal surgery. This study identified medical service use imbalance between regions for cardiovascular surgery. Results of this study demonstrate the need for political intervention to enhance the accessibility of necessary special treatment, such as cardiovascular surgery.
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Imbalance in Cardiovascular Surgery Medical Service Use Between Regions
The Korean Journal of Thoracic and Cardiovascular Surgery, 2016Co-Authors: Myunghwa Kim, Seok-jun Yoon, Ji Suk Choi, Myo Jeong Kim, Sung Bo Sim, Kun Sei Lee, Hyun Keun Chee, Nam Hee Park, Choon Seon ParkAbstract:Background: This study uses the Relevance Index to understand the condition of regional medical service use\ud for cardiovascular surgery and to identify the medical service use imbalance between regions. Methods: This\ud study calculated the Relevance Index of 16 metropolitan cities and provinces using resident registration address\ud data from the Ministry of Government Administration and Home Affairs and the 2010–2014 health insurance,\ud medical care assistance, and medical benefits claims data from the Health Insurance Review and Assessment\ud Service. We identified developments over the 5-year time period and analyzed the level of regional imbalance\ud regarding cardiovascular surgery through the relative comparison of Relevance Indexes between cardiovascular\ud and other types of surgery. Results: The Relevance Index was high in large cities such as Seoul, Daegu, and\ud Gwangju, but low in regions that were geographically far from the capital area, such as the Gangwon and\ud Jeju areas. Relevance Indexes also fell as the years passed. Cardiovascular surgery has a relatively low Relevance\ud Index compared to key types of surgery of other fields, such as neurosurgery and colorectal surgery.\ud Conclusion: This study identified medical service use imbalance between regions for cardiovascular surgery.\ud Results of this study demonstrate the need for political intervention to enhance the accessibility of necessary\ud special treatment, such as cardiovascular surgery
Laura Sani - One of the best experts on this subject based on the ideXlab platform.
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WIVACE - A Relevance Index-Based Method for Improved Detection of Malicious Users in Social Networks
Communications in Computer and Information Science, 2020Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Paolo Fornacciari, Michele Tomaiuolo, Stefano CagnoniAbstract:The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
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a Relevance Index based method for improved detection of malicious users in social networks
Workshop Artificial Life and Evolutionary Computation, 2019Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Paolo Fornacciari, Michele Tomaiuolo, Stefano CagnoniAbstract:The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
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From complex system analysis to pattern recognition: experimental assessment of an unsupervised feature extraction method based on the Relevance Index metrics
Computation, 2019Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Stefano CagnoniAbstract:The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.
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WIVACE - An improved Relevance Index method to search important structures in complex systems
Communications in Computer and Information Science, 2019Co-Authors: Laura Sani, Alberto Bononi, Riccardo Pecori, Michele Amoretti, Monica Mordonini, Andrea Roli, Marco Villani, Stefano Cagnoni, Roberto SerraAbstract:We present an improvement of a method that aims at detecting important dynamical structures in complex systems, by identifying subsets of elements that show tight and coordinated interactions among themselves, while interplaying much more loosely with the rest of the system. Such subsets are estimated by means of a Relevance Index (RI), which is normalized with respect to a homogeneous system, usually described by independent Gaussian variables, as a reference. The strategy presented herein improves the way the homogeneous system is conceived from a theoretical viewpoint. Firstly, we consider the system components as dependent and with equal pairwise correlations, which implies a non-diagonal correlation matrix of the homogeneous system. Then, we generate the components of the homogeneous system according to a multivariate Bernoulli distribution, by exploiting the NORTA method, which is able to create samples of a desired random vector, given its marginal distributions and its correlation matrix. The proposed improvement on the RI method has been applied to three different case studies, obtaining better results compared with the traditional method based on the homogeneous system with independent Gaussian variables.
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An iterative information-theoretic approach to the detection of structures in complex systems
Complexity, 2018Co-Authors: Marco Villani, Laura Sani, Riccardo Pecori, Michele Amoretti, Monica Mordonini, Andrea Roli, Roberto Serra, Stefano CagnoniAbstract:Systems that exhibit complex behaviours often contain inherent dynamical structures which evolve over time in a coordinated way. In this paper, we present a methodology based on the Relevance Index method aimed at revealing the dynamical structures hidden in complex systems. The method iterates two basic steps: detection of relevant variable sets based on the computation of the Relevance Index, and application of a sieving algorithm, which refines the results. This approach is able to highlight the organization of a complex system into sets of variables, which interact with one another at different hierarchical levels, detected, in turn, in the different iterations of the sieve. The method can be applied directly to systems composed of a small number of variables, whereas it requires the help of a custom metaheuristic in case of systems with larger dimensions. We have evaluated the potential of the method by applying it to three case studies: synthetic data generated by a nonlinear stochastic dynamical system, a small-sized and well-known system modelling a catalytic reaction, and a larger one, which describes the interactions within a social community, that requires the use of the metaheuristic. The experiments we made to validate the method produced interesting results, effectively uncovering hidden details of the systems to which it was applied.
Myunghwa Kim - One of the best experts on this subject based on the ideXlab platform.
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Imbalance in Cardiovascular Surgery Medical Service Use Between Regions.
The Korean journal of thoracic and cardiovascular surgery, 2016Co-Authors: Myunghwa Kim, Seok-jun Yoon, Ji Suk Choi, Myo Jeong Kim, Sung Bo Sim, Kun Sei Lee, Hyun Keun Chee, Nam Hee Park, Choon Seon ParkAbstract:This study uses the Relevance Index to understand the condition of regional medical service use for cardiovascular surgery and to identify the medical service use imbalance between regions. This study calculated the Relevance Index of 16 metropolitan cities and provinces using resident registration address data from the Ministry of Government Administration and Home Affairs and the 2010-2014 health insurance, medical care assistance, and medical benefits claims data from the Health Insurance Review and Assessment Service. We identified developments over the 5-year time period and analyzed the level of regional imbalance regarding cardiovascular surgery through the relative comparison of Relevance Indexes between cardiovascular and other types of surgery. The Relevance Index was high in large cities such as Seoul, Daegu, and Gwangju, but low in regions that were geographically far from the capital area, such as the Gangwon and Jeju areas. Relevance Indexes also fell as the years passed. Cardiovascular surgery has a relatively low Relevance Index compared to key types of surgery of other fields, such as neurosurgery and colorectal surgery. This study identified medical service use imbalance between regions for cardiovascular surgery. Results of this study demonstrate the need for political intervention to enhance the accessibility of necessary special treatment, such as cardiovascular surgery.
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Imbalance in Cardiovascular Surgery Medical Service Use Between Regions
The Korean Journal of Thoracic and Cardiovascular Surgery, 2016Co-Authors: Myunghwa Kim, Seok-jun Yoon, Ji Suk Choi, Myo Jeong Kim, Sung Bo Sim, Kun Sei Lee, Hyun Keun Chee, Nam Hee Park, Choon Seon ParkAbstract:Background: This study uses the Relevance Index to understand the condition of regional medical service use\ud for cardiovascular surgery and to identify the medical service use imbalance between regions. Methods: This\ud study calculated the Relevance Index of 16 metropolitan cities and provinces using resident registration address\ud data from the Ministry of Government Administration and Home Affairs and the 2010–2014 health insurance,\ud medical care assistance, and medical benefits claims data from the Health Insurance Review and Assessment\ud Service. We identified developments over the 5-year time period and analyzed the level of regional imbalance\ud regarding cardiovascular surgery through the relative comparison of Relevance Indexes between cardiovascular\ud and other types of surgery. Results: The Relevance Index was high in large cities such as Seoul, Daegu, and\ud Gwangju, but low in regions that were geographically far from the capital area, such as the Gangwon and\ud Jeju areas. Relevance Indexes also fell as the years passed. Cardiovascular surgery has a relatively low Relevance\ud Index compared to key types of surgery of other fields, such as neurosurgery and colorectal surgery.\ud Conclusion: This study identified medical service use imbalance between regions for cardiovascular surgery.\ud Results of this study demonstrate the need for political intervention to enhance the accessibility of necessary\ud special treatment, such as cardiovascular surgery
Riccardo Pecori - One of the best experts on this subject based on the ideXlab platform.
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WIVACE - A Relevance Index-Based Method for Improved Detection of Malicious Users in Social Networks
Communications in Computer and Information Science, 2020Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Paolo Fornacciari, Michele Tomaiuolo, Stefano CagnoniAbstract:The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
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a Relevance Index based method for improved detection of malicious users in social networks
Workshop Artificial Life and Evolutionary Computation, 2019Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Paolo Fornacciari, Michele Tomaiuolo, Stefano CagnoniAbstract:The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
-
From complex system analysis to pattern recognition: experimental assessment of an unsupervised feature extraction method based on the Relevance Index metrics
Computation, 2019Co-Authors: Laura Sani, Riccardo Pecori, Monica Mordonini, Stefano CagnoniAbstract:The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.
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WIVACE - An improved Relevance Index method to search important structures in complex systems
Communications in Computer and Information Science, 2019Co-Authors: Laura Sani, Alberto Bononi, Riccardo Pecori, Michele Amoretti, Monica Mordonini, Andrea Roli, Marco Villani, Stefano Cagnoni, Roberto SerraAbstract:We present an improvement of a method that aims at detecting important dynamical structures in complex systems, by identifying subsets of elements that show tight and coordinated interactions among themselves, while interplaying much more loosely with the rest of the system. Such subsets are estimated by means of a Relevance Index (RI), which is normalized with respect to a homogeneous system, usually described by independent Gaussian variables, as a reference. The strategy presented herein improves the way the homogeneous system is conceived from a theoretical viewpoint. Firstly, we consider the system components as dependent and with equal pairwise correlations, which implies a non-diagonal correlation matrix of the homogeneous system. Then, we generate the components of the homogeneous system according to a multivariate Bernoulli distribution, by exploiting the NORTA method, which is able to create samples of a desired random vector, given its marginal distributions and its correlation matrix. The proposed improvement on the RI method has been applied to three different case studies, obtaining better results compared with the traditional method based on the homogeneous system with independent Gaussian variables.
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An iterative information-theoretic approach to the detection of structures in complex systems
Complexity, 2018Co-Authors: Marco Villani, Laura Sani, Riccardo Pecori, Michele Amoretti, Monica Mordonini, Andrea Roli, Roberto Serra, Stefano CagnoniAbstract:Systems that exhibit complex behaviours often contain inherent dynamical structures which evolve over time in a coordinated way. In this paper, we present a methodology based on the Relevance Index method aimed at revealing the dynamical structures hidden in complex systems. The method iterates two basic steps: detection of relevant variable sets based on the computation of the Relevance Index, and application of a sieving algorithm, which refines the results. This approach is able to highlight the organization of a complex system into sets of variables, which interact with one another at different hierarchical levels, detected, in turn, in the different iterations of the sieve. The method can be applied directly to systems composed of a small number of variables, whereas it requires the help of a custom metaheuristic in case of systems with larger dimensions. We have evaluated the potential of the method by applying it to three case studies: synthetic data generated by a nonlinear stochastic dynamical system, a small-sized and well-known system modelling a catalytic reaction, and a larger one, which describes the interactions within a social community, that requires the use of the metaheuristic. The experiments we made to validate the method produced interesting results, effectively uncovering hidden details of the systems to which it was applied.