Multi-Objective Evolutionary Fuzzy Clustering for High-Dimensional Problems

Nuovo, Alessandro Di; Palesi, Maurizio; Catania, Vincenzo

doi:10.1109/fuzzy.2007.4295660

Cited by 9 publications

(12 citation statements)

References 36 publications

(36 reference statements)

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“…{feature 6} ⊂ {feature 6, feature 9, feature 13} ⊂ {feature 6, feature 9, feature 10, feature 13}. (6) Here, the corresponding features are as follows: 6: average number of rooms per dwelling, 9: index of accessibility to radial highways, 13: percentage of lower status population, and 10: full-value property-tax rate per $10,000. This combination is quite convincing.…”

Section: Results Of the Experimentsmentioning

confidence: 99%

“…In addition, the main drawback is the fuzzy models' relative inefficiency as the size of the data increases, regarding both the number of data points in the data set and the number of features. Moreover, one of the most widely used approaches in fuzzy modeling is the fuzzy C-means (FCM) algorithm for constructing the antecedents of the rules associated with the curse of dimensionality [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

Ahmad

Pedrycz

2012

Applied Computational Intelligence and Soft Computing

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The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

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Section: Results Of the Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

Ahmad

Pedrycz

2012

Applied Computational Intelligence and Soft Computing

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“…In the earlier approach proposed in [9], Genetic Algorithm (GA) was used to simultaneously select the suitable data points and features for a classification problem. Recently, in [14] the researchers employed a simulated annealing approach to alternate instance selection and feature selection during each step of the search. Finally, more complex approaches combining the data and feature selection were developed in [10,11].…”

Section: A Feature and Instance Selectionmentioning

confidence: 99%

Feature and instance selection via cooperative PSO

Ahmad

Pedrycz

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Advances in data collection and storage capabilities during the past decades have led to an information overload in most application domains. The huge amount of data the realworld applications has necessitated the use of a reduction mechanism. The reduction method contains two main techniques: feature selection and instance selection, which are usually applied individually. Although, some work has been done to implement the feature and instance selection simultaneously, this work has focused on mainly the classification problem. This paper proposes the integration of feature selection and instance selection for solving the regression problem by using the fuzzy modeling approach. The selection of features and instances is based on the cooperative particle swarm optimization technique, which aims to limit the effect of the curse of dimensionality that occurs when dealing with the high dimensionality of the search space. The proposed method is applied to three real-world datasets from the machine learning repository. The algorithm's performance is illustrated by the corresponding plots of the prediction error for the different amounts of data being selected.

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“…Similarly, many MOPs have a huge number of variables (more than 100 variables [30]); some examples are classification [31], clustering [32], recommendation systems [33], and so on. However, the goal of traditional MOEAs is to solve multi-objective small-scale optimization problems (MOSSOPs); consequently, the traditional algorithms may be incapable of tackling multiobjective large-scale optimization problems (MOLSOPs) because of the "curse of dimensionality".…”

Section: Introductionmentioning

confidence: 99%

Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

Cao

Zhao

Yang

et al. 2018

Future Generation Computer Systems

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Using immune algorithms is generally a time-intensive process-especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large number of variables are divided into several groups. Finally, individual evaluations are allocated across many core processing units, and calculations are performed in parallel. Consequently, the computation time is greatly reduced. The proposed algorithm integrates the idea of immune algorithms, which tend to explore sparse areas in the objective space and use simulated binary crossover for mutation. The proposed algorithm is employed to optimize the 3D terrain

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Multi-Objective Evolutionary Fuzzy Clustering for High-Dimensional Problems

Cited by 9 publications

References 36 publications

Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

Feature and instance selection via cooperative PSO

Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

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