| dc.contributor | FELIPE ORIHUELA ESPINA | |
| dc.contributor | CARLOS ALBERTO REYES GARCIA | |
| dc.creator | Andrea Burgos Madrigal | |
| dc.date | 2018-01 | |
| dc.date.accessioned | 2023-07-25T16:23:17Z | |
| dc.date.available | 2023-07-25T16:23:17Z | |
| dc.identifier | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1250 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7806447 | |
| dc.description | This thesis looks at the problem of ordering of functions, here referred to as components, over multiple fuzzy criteria. Current solutions require explicit quantification of the relevance of the criteria to the ordering which may be unavailable. We hypothesized that the relevance can be encoded in a weigthed strategy such that the resulting orderingof the components approaches that which an expert would have done. The solutionhere relies on a new set of membership functions to the criteria and the incorporation to the ordering relation of intensifiers to yield the weighting strategy. The new ordering relation is applied electroencephalography (EEG) where the relevance of independent components to certain neuroscientific process has to be determined. Three new fuzzy membership functions are proposed; knowledge based, prototype based and distribution based. Validity of the new membership functions is established by showing membership values convergent (high values for components close to a criteria) and divergent (low values for components unrelated to the criteria) over synthetic data. Membership functions sustained a tolerance of noise up to 0.25, 0.4 and 0.2 for knowledge, prototype and distribution based respectively before affecting the ordering. Two weightings strategies mapping qualitative appreciations to quantitative contributions (contrast modifiers and linguistic hedges) are tested, and their performance is compared to two explicit weighting strategies (unweighted, additive). The suggested weighting methods changed the determinant of the mixture matrix (Friedmann: < 0.05). Compared to the unweighted, linguistic hedes showed higher similarity to the aimed order. Finally, a new order relation is proposed by integrating weighting, equalization and lifting to the comparison process. The performance of the new ordering relation is assessed on two ordering tasks over EEG datasets, one on imagined speech, and the other from an attentional taks labelled by an expert psychologist. Performance was assessed through classification wrapping and compared to ordering obtained with the Hurst method, proposed ad-hoc for ordering components of the imagined speech dataset. In both datasets, the clasiffication after ordering is above chance; being closer to the Hurst method (than to chance) for the imagined speech dataset, and statistically indistinguishable to this standard when accuracy rates are compared in the attention dataset. | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| dc.relation | citation:Burgos Madrigal, A. (2018). Ordering of functions according to multiple fuzzy criteria: application to electroencephalography, Tesis de Maestría, Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.subject | info:eu-repo/classification/Relaciones borrosas/Fuzzy relations | |
| dc.subject | info:eu-repo/classification/Análisis de decisión/Decision analysis | |
| dc.subject | info:eu-repo/classification/Evaluación multicriterio/Multicriteria evaluation | |
| dc.subject | info:eu-repo/classification/Electroencefalografía/Electroencephalography | |
| dc.subject | info:eu-repo/classification/cti/1 | |
| dc.subject | info:eu-repo/classification/cti/12 | |
| dc.subject | info:eu-repo/classification/cti/1203 | |
| dc.subject | info:eu-repo/classification/cti/120323 | |
| dc.subject | info:eu-repo/classification/cti/120323 | |
| dc.title | Ordering of functions according to multiple fuzzy criteria: application to electroencephalography | |
| dc.type | info:eu-repo/semantics/masterThesis | |
| dc.type | info:eu-repo/semantics/acceptedVersion | |
| dc.audience | students | |
| dc.audience | researchers | |
| dc.audience | generalPublic | |
