GPU-Based Parallel Search of Relevant Variable Sets in Complex Systems

Vicari, Emilio; Amoretti, Michele; Sani, Laura; Mordonini, Monica; Pecori, Riccardo; Roli, Andrea; Villani, Marco; Cagnoni, Stefano; Serra, Roberto

doi:10.1007/978-3-319-57711-1_2

Cited by 13 publications

(14 citation statements)

References 11 publications

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“…In this respect, we can note that while the collection of time series is an experimentally difficult and costly task, the RI methodology can be applied merely by comparing different steady states (whose data could derive even from different beings), in such a way taking advantage from more common data sources. In case experimental data are available, the RI method can provide an effective idea of the dynamical organization of the observed system without requiring any knowledge of topology, dynamical rules, or parameters [26,31,32]. Fig.…”

Section: Ri Resultsmentioning

confidence: 99%

“…where RI h and σ(RI h ) are, respectively, the average and the standard deviation of the RI of a sample of subsets of size k extracted from a reference homogeneous system U h , and ν = MI h / I h is its normalization constant. A more detailed description can be found in previous work [26,31]. The generation of the homogeneous system is critical, and often, in past papers, a simple but general and easy to compute solution was chosen.…”

Section: Methodsmentioning

confidence: 99%

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A Relevance Index Method to Infer Global Properties of Biological Networks

Villani

Sani

Amoretti

et al. 2018

Communications in Computer and Information Science

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Section: Ri Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Relevance Index Method to Infer Global Properties of Biological Networks

Villani

Sani

Amoretti

et al. 2018

Communications in Computer and Information Science

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“…The computation of T c itself is a rather lengthy procedure. Therefore, we tried to limit the computation time needed to run our method, on the one hand, by implementing the T c computation algorithm as massively parallel GPU code [8], while on the other hand, by designing a metaheuristic, in which a genetic algorithm is hybridized with a local search. The latter is described in detail in the following subsections.…”

Section: Methodsmentioning

confidence: 99%

“…The RI method has been applied with interesting results to several systems: some of them had been artificially designed in order to test the effectiveness of the technique, while others referred to interesting physical, chemical, biological, or socio-economic systems [6,7]. In addition, the efficiency of the method has also been improved by using a parallel implementation of the RI computation [8] and some metaheuristics to deal with the "curse of dimensionality" when analysing high-dimensional systems [9,10]. In general, the method can be applied every time a collection of observations of the values of the system variables at different instants or conditions is available.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Information‐Theoretic Approach to the Detection of Structures in Complex Systems

et al. 2018

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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.

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“…From now on, in this paper, we refer to the unsupervised zI-based method we tested as ZIFF (zI-based Feature Finder). In ZIFF, the zI computation is carried out by a specifically-designed evolutionary algorithm [4,5], to counteract the curse of dimensionality and find the most relevant variable subsets in reasonable time, given the exponential complexity of an exhaustive search with respect to the number of variables. To further aggregate the variable sets and discard redundant ones, we adopt the iterative sieving procedure described in [6].…”

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confidence: 99%

From Complex System Analysis to Pattern Recognition: Experimental Assessment of an Unsupervised Feature Extraction Method Based on the Relevance Index Metrics

et al. 2019

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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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GPU-Based Parallel Search of Relevant Variable Sets in Complex Systems

Cited by 13 publications

References 11 publications

A Relevance Index Method to Infer Global Properties of Biological Networks

A Relevance Index Method to Infer Global Properties of Biological Networks

An Iterative Information‐Theoretic Approach to the Detection of Structures in Complex Systems

From Complex System Analysis to Pattern Recognition: Experimental Assessment of an Unsupervised Feature Extraction Method Based on the Relevance Index Metrics

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