A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

Filatovas, Ernestas; Podkopaev, Dmitry; Kurasova, Olga

doi:10.15837/ijccc.2015.4.1672

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…MDS has also been used to visualize clustered nondominated solutions during the optimization process (Kurasova et al, 2013). The procedure was later extended for interactive MOO in Filatovas et al (2015). Chemical engineering applications involving the use of MDS to understand the Paretooptimal solutions in the objective and decision spaces can be found inŽilinskas et al (2006,2015).…”

Section: The Simplest Dimensionality Reduction Technique Is Principalmentioning

confidence: 99%

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Bandaru

Deb

2017

Expert Systems with Applications

150

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Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems.

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Section: The Simplest Dimensionality Reduction Technique Is Principalmentioning

confidence: 99%

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Bandaru

Deb

2017

Expert Systems with Applications

150

View full text Add to dashboard Cite

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“…The usefulness of a priori, a posteriori, and interactive methods in multiple objective optimization are highly emphasized in the literature (see for instance [1]). A visualization technique for accessing the solution pool in the interactive methods of multiple objective optimization can be found in [2].…”

Section: Continuous Distribution Approximation and Thresholds Optimizmentioning

confidence: 99%

Continuous Distribution Approximation and Thresholds Optimization in Serial Multi-Modal Biometric Systems

Stanojević¹,

Milenkovic²,

Starčević³

et al. 2016

INT J COMPUT COMMUN

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Multi-modal biometric verification systems use information from several biometric modalities to verify an identity of a person. The false acceptance rate (FAR) and false rejection rate (FRR) are metrics generally used to measure the performance of such systems. In this paper, we first approximate the score distributions of both genuine users and impostors by continuous distributions. Then we incorporate the exact expressions of the distributions in the formulas for the expected values of both FAR and FRR for each matcher. In order to determine the upper and lower acceptance thresholds in the sequential multi-modal biometric matching, we further minimize the expected values of FAR and FRR for the entire processing chain. We propose a non-linear bi-objective programming problem whose objective functions are the two error probabilities. We analyze the efficient set of the bi-objective problem, and derive an efficient solution as a best compromise between the error probabilities. Replacing the least squares approximation of the score distributions by a continuous distribution approximation, this approach modifies the method presented in Stanojević et al. [15] (doi: 10.1109[15] (doi: 10. /ICCCC.2016 a . The results of our experiments showed a good performance of the sequential multiple biometric matching system based on continuous distribution approximation and optimized thresholds.

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“…Multidimensional Scaling (MDS) consists of a set of techniques which are used to reduce the dimensions of a data set. Such techniques are used in many applicationsmultiobjective optimization (Filatovas et al, 2015), data mining (Medvedev et al, 2017), (Bernatavičienė et al, 2007), marketing (Green, 1975), cryptography (Gupta and Ray, 2015), a wide variety of mathematical and statistical methods (Granato and Ares, 2014), psychology (Rosenberg, 2014), etc. They use a mapping function usually based on Euclidean distances which is able to find an optimal data representation.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using Isomap with SMACOF

Orts¹,

López²,

Filatovas³

et al. 2019

Informatica

Self Cite

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The isometric mapping (Isomap) algorithm is often used for analysing hyperspectral images. Isomap allows to reduce such hyperspectral images from a high-dimensional space into a lower-dimensional space, keeping the critical original information. To achieve such objective, Isomap uses the state-of-the-art MultiDimensional Scaling method (MDS) for dimensionality reduction. In this work, we propose to use Isomap with SMACOF, since SMACOF is the most accurate MDS method. A deep comparison, in terms of accuracy, between Isomap based on an eigendecomposition process and Isomap based on SMACOF has been carried out using three benchmark hyperspectral images. Moreover, for the hyperspectral image classification, three classifiers (support vector machine, k-nearest neighbour, and Random Forest) have been used to compare both Isomap approaches. The experimental investigation has shown that better classification accuracy is obtained by Isomap with SMACOF.

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A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

Cited by 8 publications

References 39 publications

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Continuous Distribution Approximation and Thresholds Optimization in Serial Multi-Modal Biometric Systems

Hyperspectral Image Classification Using Isomap with SMACOF

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