Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials

Smolinski, Tomasz G.; Boratyn, Grzegorz M.; Milanova, Mariofanna; Buchanan, Roger; Prinz, Astrid A.

doi:10.1007/11818564_19

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Finding a unique solution in this space with an explicitly model-based approach is much more efficient because the problem is reduced to a linear search, which was shown to be possible under certain experimental conditions (Wood et al 2004; Huys et al 2006). However, if such an approach cannot be taken, a common alternative to brute-force exploration is to find model parameters by minimizing a goodness of fit value either by following its gradient (Vanier and Bower 1999; Weaver and Wearne 2006) or by using an evolutionary approach (Achard and Schutter 2006; Van Geit et al 2007; Smolinski et al 2008; Van Geit et al 2008), which are both optimization algorithms. The PANDORA database approach applies to parameter optimization in two ways.…”

Section: Discussionmentioning

confidence: 99%

Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox

Günay

Edgerton

et al. 2009

Neuroinform

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Neuronal recordings and computer simulations produce ever growing amounts of data, impeding conventional analysis methods from keeping pace. Such large datasets can be automatically analyzed by taking advantage of the well-established relational database paradigm. Raw electrophysiology data can be entered into a database by extracting its interesting characteristics (e.g., firing rate). Compared to storing the raw data directly, this database representation is several orders of magnitude higher efficient in storage space and processing time. Using two large electrophysiology recording and simulation datasets, we demonstrate that the database can be queried, transformed and analyzed. This process is relatively simple and easy to learn because it takes place entirely in Matlab, using our database analysis toolbox, PANDORA. It is capable of acquiring data from common recording and simulation platforms and exchanging data with external database engines and other analysis toolboxes, which make analysis simpler and highly interoperable. PANDORA is available to be freely used and modified because it is open-source (http://software.incf.org/software/pandora/home).

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Section: Discussionmentioning

confidence: 99%

Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox

Günay

Edgerton

et al. 2009

Neuroinform

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“…Some examples of applications of CD can be found in. [21][22][23][24] In this article, we propose a hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform decomposition of two dimensional trajectories in light of the underlying classification problem itself (i.e., suspicious vs. usual trajectories). The idea is to look for basis functions whose coefficients allow for an accurate classification while preserving the reconstruction to the best possible extent.…”

Section: Methodsmentioning

confidence: 99%

Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets

et al. 2013

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Detection of unusual trajectories of moving objects (e.g., people, automobiles, etc.) is an important problem in many civilian and military surveillance applications. In this work, we propose a multi-objective evolutionary algorithms and rough sets-based approach that breaks down 2-dimensional trajectories into a set of additive components, which then can be used to build a classifier capable of recognizing typical, but yet unseen trajectories, and identifying those that seem suspicious.

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“…The description of previous stages of the study and some examples of applications can be found in [2-5]. In this article, we investigate hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform decomposition in the light of the classification problem itself.…”

Section: Introductionmentioning

confidence: 99%

“…We propose a simple extension of the well-known multi-objective evolutionary algorithm VEGA [6], which we call end-VEGA (elitist-non-dominated-VEGA). The extension, in its initial form introduced in [5], supplies the algorithm with the considerations related to elitism and non-dominance, lack of which is known to be its main drawback [7]. We also investigate the idea of utilizing ICA to initialize the population in the MOEA.…”

Section: Introductionmentioning

confidence: 99%

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

et al. 2006

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BackgroundIndependent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness."ResultsThe preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis.ConclusionWe present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself.

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Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials

Cited by 9 publications

References 7 publications

Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox

Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox

Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

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