Selección de prototipos basada en conjuntos rugosos difusos

En este trabajo abordamos uno de los principales problemas de k vecinos más cercanos (kNN): su sensibilidad al ruido. Llevamos a cabo Selección de Prototipos (SP), es decir, eliminamos instancias ruidosas para mejorar la calidad de la clasificación de k vecinos más cercanos. Concretamente, basándonos en un método existente de selección de instancias basada en conjuntos rugosos difusos, construimos un algoritmo de tipo envoltura que tiene en cuenta la granularidad óptima de la relación difusa de indiscernibilidad en cada conjunto de datos. Llamamos a este método Selección de Prototipos a base de Conjuntos Aproximados Difusos (SPCAD). La comparación del enfoque con el estado del arte en Selección de Prototipos confirma que nuestro método ofrece buenos resultados: supera a todos los métodos de selección de prototipos existentes con respeto a la precisión de la clasificación

Failure mode & effect analysis for improving data veracity and validity

Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications, including electronics manufacturing. In this paper we propose a modification of this method to extend its application for data veracity and validity improvement. The proposed DVV-FMEA method is based on engineering features and in addition, provides transparency and understandability of the data and its pre-processing, making it reproducible and trustful

Impact of Diet and Quality Grade on Shelf Life of Beef Steaks

Steers were fed a diet containing dry rolled corn, steam flaked corn, dry rolled corn with 30% dried distillers grains, or steam flaked corn with 30% dried distillers grains. Strip loins from upper 2/3 Choice and Select- grade carcasses were obtained to evaluate the effects of diet and quality grade on shelf life characteristics. Strip loins were aged for 2, 9, 16, or 23 days. Results suggest that steaks from cattle fed steam flaked corn (with or without dried distillers grains) and from cattle fed dried distillers grains (regardless of corn type) had higher levels of many unsaturated fatty acids, more discoloration, and greater lipid oxidation compared to the dry rolled corn treatments or the no dried distillers grains treatments, respectively. Feeding of dry rolled corn or diets without dried distillers grains maintained red color better during retail display. Choice- grade steaks had significantly higher levels of unsaturated fatty acids like 18:2 and total polyunsaturated fatty acids than Select- grade steaks but did not diff er in color stability or oxidation. These data indicate the longest shelf life will occur when cattle are fed diets containing dry rolled corn (versus steam flaked corn) or without dried distillers grains (versus with dried distillers grains) and that both steam flaked corn and distillers grains have a negative impact on shelf life. Quality grade did not affect color stability

Trust and Distrust Aggregation Enhanced with Path Length Incorporation

Trust networks are social networks in which users can assign trust scores to each other. In order to estimate these scores for agents that are indirectly connected through the network, a range of trust score aggregators has been proposed. Currently, none of them takes into account the length of the paths that connect users; however, this appears to be a critical factor since longer paths generally contain less reliable information. In this paper, we introduce and evaluate several path length incorporating aggregation strategies in order to strike the right balance between generating more predictions on the one hand and maintaining a high prediction accuracy on the other hand.European Union (EU) TIN2010-17876; TIC-5299; TIC-05991FW

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition : a fuzzy rough set approach

Class imbalance occurs when data elements are unevenly distributed among classes, which poses a challenge for classifiers. The core focus of the research community has been on binary-class imbalance, although there is a recent trend toward the general case of multi-class imbalanced data. The IFROWANN method, a classifier based on fuzzy rough set theory, stands out for its performance in two-class imbalanced problems. In this paper, we consider its extension to multi-class data by combining it with one-versus-one decomposition. The latter transforms a multi-class problem into two-class sub-problems. Binary classifiers are applied to these sub-problems, after which their outcomes are aggregated into one prediction. We enhance the integration of IFROWANN in the decomposition scheme in two steps. Firstly, we propose an adaptive weight setting for the binary classifier, addressing the varying characteristics of the sub-problems. We call this modified classifier IFROWANN-WIR. Second, we develop a new dynamic aggregation method called WV–FROST that combines the predictions of the binary classifiers with the global class affinity before making a final decision. In a meticulous experimental study, we show that our complete proposal outperforms the state-of-the-art on a wide range of multi-class imbalanced datasets

Failure mode & effect analysis and another methodology for improving data veracity and validity

Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications. In this paper we extend its usage for data veracity and validity improvement in the context of big data analysis and discuss its application in an electronics manufacturing test procedure which consists of a sequence of tests. Finally, we describe another methodology, developed as a result of the DVV-FMEA application which is aimed at improving the tests' repeatability and failure detection capabilities as well as monitoring their reliability

Impact of Dietary Fat Source on Beef Tenderness

Steers were finished on either a corn control, 40% full-fat modified distillers grains plus solubles, 40% de-oiled modified distillers grains plus solubles, or 38% de-oiled modified distillers grains plus solubles plus 2% corn oil diet to evaluate the effects of dietary fat source on the mechanism of beef tenderization . Feeding modified distillers grains plus solubles increased polyunsaturated fatty acid content in the sarcoplasmic reticulum membrane and increased free Ca2+ concentration early postmortem. Steaks from cattle fed de-oiled modified distillers grains and de-oiled modified distillers grains plus corn oil were more tender at 2 d of aging when compared to corn control diet. These data indicate that feeding modified distillers grains plus solubles to cattle has the potential to increase beef tenderness early postmortem in comparison to corn diets

Eutrofización costera en la Península de Yucatán

28 páginas, 12 figuras, 2 tablas.-- Artículo incluido en el Cap.IV: Manejo de la Zona Costera del Golfo de México.El financiamiento ha provenido del CONACYT (PO20COOR; 4147PT; 32356T; SISIERRA-2000-07-06-15), CONABIO (B019; M011; S004) y CINVESTAV-IPN Unidad Merida.Peer reviewe