Development of child's home environment indexes based on consistent families of aggregation operators with prioritized hierarchical information

Rojas, Karina; Gómez, Daniel; Montero, Javier; Rodríguez, J. Tinguaro; Valdivia, Andrea; Paiva, Francisco Cleiton da Silva

doi:10.1016/j.fss.2013.06.007

Cited by 15 publications

(2 citation statements)

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“…We propose here an algorithm based on the approach presented in Flores-Vidal et al (2017) and Rojas et al (2014), which instead of just minimizing the sum of distances of the segments to their centroids as in fuzzy c-means algorithm, it is based on a multi-criteria problem that focuses on identifying the cluster centroids by taking into account three quality measures (Amo et al 2001):…”

Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 99%

A new edge detection method based on global evaluation using fuzzy clustering

et al. 2018

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Traditionally, the edge detection process requires one final step that is known as scaling. This is done to decide, pixel by pixel, if these will be selected as final edge or not. This can be considered as a local evaluation method that presents practical problems, since the edge candidate pixels should not be considered as independent. In this article, we propose a strategy to solve these problems through connecting pixels that form arcs, that we have called segments. To accomplish this, our edge detection algorithm is based on a more global evaluation inspired by actual human vision. Our paper further develops ideas 1 first proposed in Venkatesh and Rosin (Graph Models Image Process 57(2):146-160, 1995). These segments contain visual features similar to those used by humans, which lead to better comparative results against humans. In order to select the relevant segments to be retained, we use fuzzy clustering techniques. Finally, this paper shows that this fuzzy clustering of segments presents a higher performance compared to other standard edge detection algorithms. KeywordsEdge detection • Global evaluation • Supervised classification • Fuzzy clustering • Edge segments Edge detection is quite useful in many fields. For instance, for the recognition of different pathologies in medical diagnoses (Sonka 2014), a field has grown in recent years. It is also used in images taken by satellites or drones-remote sensing-for agricultural purposes. Some other relevant fields of application are the military industry, law enforcement, among others (Monga et al. 1991; Fathy and Siyal 1995; Zielke et al. Communicated by I. Perfilieva.

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Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 99%

A new edge detection method based on global evaluation using fuzzy clustering

et al. 2018

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“…Strict stability (among other properties) should also take into account that the data may present some structure (see 17,13 for more details). In the next section, we present some definitions of strict stability for nonsymmetric FAOs that will be dependent on the structure of the data to be aggregated.…”

Section: On I-l and J-r Stabilitymentioning

confidence: 99%

Consistency and stability in aggregation operators: An application to missing data problems

Gómez¹,

Rojas²,

Montero³

et al. 2014

IJCIS

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In this work we analyze the key issue of the relationship that should hold between the operators in a family {A n } of aggregation operators in order to understand they properly define a consistent whole. Here we extend some of the ideas about stability of a family of aggregation operators into a more general framework, formally defining the notions of i − L and j − R strict stability for families of aggregation operators. The notion of strict stability of order k is introduced as well. Finally, we also present an application of the strict stability conditions to deal with missing data problems in an information aggregation process. For this analysis, we have focused in the weighted mean family and the quasi-arithmetic weighted means families.

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