Author: JESUS ANTONIO GONZALEZ BERNAL

Extracting new patterns for cardiovascular disease prognosis

LUIS MENA CAMARE JESUS ANTONIO GONZALEZ BERNAL (2009)

Cardiovascular diseases constitute one of the main causes of mortality in the world, and machine learning has become a powerful tool for analysing medical data in the last few years. In this paper we present an interdisciplinary work based on an ambulatory blood pressure study and the development of a new classification algorithm named REMED. We focused on the discovery of new patterns for abnormal blood pressure variability as a possible cardiovascular risk factor. We compared our results with other classification algorithms based on Bayesian methods, decision trees, and rule induction techniques. In the comparison, REMED showed similar accuracy to these algorithms but it has the advantage of being superior in its capacity to classify sick people correctly. Therefore, our method could represent an innovative approach that might be useful in medical decision support for cardiovascular disease prognosis.

Article

Cardiovascular diseases Machine learning Blood pressure variability Classification Medical decision support Prognosis CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES

Symbolic one-class learning from imbalanced datasets: Application in medical diagnosis

LUIS JAVIER MENA CAMARE JESUS ANTONIO GONZALEZ BERNAL (2009)

When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem; we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.

Article

Machine learning Imbalanced datasets One-class learning Classification algorithm Rule extraction CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES

Minería de datos con información de contexto para la clasificación de imágenes satelitales

Data mining with context information for satellite image classification

JESUS ANTONIO GONZALEZ BERNAL LEOPOLDO ALTAMIRANO ROBLES JUAN FRANCISCO ROBLES CAMACHO (2008)

En este artículo se presenta un esquema de clasificación multi-modelos para imágenes satelitales apoyado con información de contexto con el que se mejora la precisión de una pre-clasificación obtenida con algoritmos paramétricos. El nuevo esquema utiliza una red semántica como representación de conocimiento que almacena patrones creados con un ensamble de árboles de decisión (alimentado con características espectrales, de textura y geométricas para describir a las regiones de interés) y por otro lado patrones espaciales creados a partir de una representación basada en grafos (con información de contexto a partir de relaciones espaciales entre las regiones de interés). Los resultados experimentales muestran que el esquema de clasificación propuesto mejora la precisión de la pre-clasificación de los algoritmos paramétricos al utilizar información de contexto.

This paper presents a multi-model classification schema for satellite images supported with context information to enhance the accuracy of a pre-classification obtained with parametric algorithms. This new scheme uses a semantic network as knowledge representation that stores the patterns created with a decision tree ensemble (fed with spectral, texture and geometric descriptive characteristics to describe the regions of interest) and spatial patterns created with a graph-based representation (with context information obtained from spatial relations among regions of interest). Our experimental results show that the proposed classifi cation scheme enhances the pre-classification accuracy obtained with parametric algorithms when we use context information.

Article

Percepción remota Mapas temáticos Minería de datos Clasificación Información de contexto Remote sensing Thematic maps Data mining Classification Context information CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES

Neural networks and SVM-based classification of leukocytes using the morphological pattern spectrum

JUAN MANUEL RAMIREZ CORTES MARIA DEL PILAR GOMEZ GIL VICENTE ALARCON AQUINO JESUS ANTONIO GONZALEZ BERNAL ANGEL MARIO GARCIA PEDRERO (2010)

In this paper we present the morphological operator pecstrum, or pattern spectrum, as a feature extractor of discriminating characteristics in microscopic leukocytes images for classification purposes. Pecstrum provides an excellent quantitative analysis to model the morphological evolution of nuclei in blood white cells, or leukocytes. According to their maturity stage, leukocytes have been classified by medical experts in six categories, from myeloblast to polymorphonuclear corresponding to the youngest and oldest extremes, respectively. A feature vector based on the pattern spectrum, normalized area, and nucleus - cytoplasm area ratio, was tested using a multilayer perceptron neural network trained by backpropagation, and a Support Vector Machine algorithm. Results from Euclidean distance and k-nearest neighbor classifiers are also reported as reference for comparison purposes. A recognition rate of 87% was obtained in the best case, using 36 patterns for training and 18 for testing, with a three-fold validation scheme. Additional experiments exploring larger databases are currently in progress.

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA FÍSICA ELECTRÓNICA ELECTRÓNICA

Acute leukemia classification by ensemble particle swarm model selection

Hugo Jair Escalante Balderas Manuel Montes y Gómez Jesús Antonio González Bernal María del Pilar Gómez Gil Leopoldo Altamirano Robles CARLOS ALBERTO REYES GARCIA CAROLINA RETA CASTRO ALEJANDRO ROSALES PEREZ (2012)

Objective: Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. Methods and materials: This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the search space of ensembles that can be formed by heterogeneous classification models in a machine learning toolbox. EPSMS does not require prior domain knowledge and it is able to select highly accurate classification models without user intervention. Furthermore, specific models can be used for different classification tasks. Results: We report experimental results for acute leukemia classification with real data and show that EPSMS outperformed the best results obtained using manually designed classifiers with the same data. The highest performance using EPSMS was of 97.68% for two-type classification problems and of 94.21% for more than two types problems. To the best of our knowledge, these are the best results reported for this data set. Compared with previous studies, these improvements were consistent among different type/subtype classification tasks, different features extracted from images, and different feature extraction regions. The performance improvements were statistically significant.Weimproved previous results by an average of 6% and there are improvements of more than 20% with some settings. In addition to the performance improvements, we demonstrated that no manual effort was required during acute leukemia type/subtype classification.

Conclusions: Morphological classification of acute leukemia usingEPSMSprovides an alternative to expensive diagnostic methods in developing countries. EPSMS is a highly effective method for the automated construction of ensemble classifiers for acute leukemia classification, which requires no significant user intervention. EPSMS could also be used to address other medical classification tasks.

Article

Ensemble learning Swarm optimization Full model selection Morphological classification Analysis of bone marrow cell images Acute leukemia classification CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES CIENCIA DE LOS ORDENADORES

The segmented and annotated IAPR TC-12 benchmark

HUGO JAIR ESCALANTE BALDERAS CARLOS ARTURO HERNANDEZ GRACIDAS JESUS ANTONIO GONZALEZ BERNAL AURELIO LOPEZ LOPEZ MANUEL MONTES Y GOMEZ EDUARDO FRANCISCO MORALES MANZANARES LUIS ENRIQUE SUCAR SUCCAR LUIS VILLASEÑOR PINEDA (2009)

Automatic image annotation (AIA), a highly popular topic in the field of information retrieval research, has experienced significant progress within the last decade. Yet, the lack of a standardized evaluation platform tailored to the needs of AIA, has hindered effective evaluation of its methods, especially for region-based AIA. Therefore in this paper, we introduce the segmented and annotated IAPR TC-12 benchmark; an extended resource for the evaluation of AIA methods as well as the analysis of their impact on multimedia information retrieval. We describe the methodology adopted for the manual segmentation and annotation of images, and present statistics for the extended collection. The extended collection is publicly available and can be used to evaluate a variety of tasks in addition to image annotation. We also propose a soft measure for the evaluation of annotation performance and identify future research areas in which this extended test collection is likely to make a contribution.

Article

Data set creation Ground truth collection Evaluation metrics Automatic image annotation Image retrieval CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES

The segmented and annotated IAPR TC-12 benchmark

HUGO JAIR ESCALANTE BALDERAS CARLOS ARTURO HERNANDEZ GRACIDAS JESUS ANTONIO GONZALEZ BERNAL AURELIO LOPEZ LOPEZ MANUEL MONTES Y GOMEZ EDUARDO FRANCISCO MORALES MANZANARES LUIS ENRIQUE SUCAR SUCCAR LUIS VILLASEÑOR PINEDA (2010)

Automatic image annotation (AIA), a highly popular topic in the field of information retrieval research, has experienced significant progress within the last decade. Yet, the lack of a standardized evaluation platform tailored to the needs of AIA, has hindered effective evaluation of its methods, especially for region-based AIA. Therefore in this paper, we introduce the segmented and annotated IAPR TC-12 benchmark; an extended resource for the evaluation of AIA methods as well as the analysis of their impact on multimedia information retrieval. We describe the methodology adopted for the manual segmentation and annotation of images, and present statistics for the extended collection. The extended collection is publicly available and can be used to evaluate a variety of tasks in addition to image annotation. We also propose a soft measure for the evaluation of annotation performance and identify future research areas in which this extended test collection is likely to make a contribution.

Article

Data set creation Ground truth collection Evaluation metrics Automatic image annotation Image retrieval CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES CIENCIA DE LOS ORDENADORES

La vid silvestre en México. Actualidades y potencial

SARA AGUIRRE ORTEGA JUAN GUILLERMO CRUZ CASTILLO MARIA DE LA CRUZ ESPINDOLA BARQUERA OMAR FRANCO MORA ANDRES GONZALEZ HUERTA ANA TARIN GUTIERREZ IBAÑEZ ANTONIO LAGUNA CERDA EDGAR JESUS MORALES ROSALES DELFINA DE JESUS PEREZ LOPEZ JUAN CARLOS REYES ALEMAN DELFINO REYES LOPEZ MARTIN RUBI ARRIAGA JUAN SALOMON CASTAÑO JESUS RICARDO SANCHEZ PALE J. REFUGIO TOBAR REYES JUAN MANUEL VILLARREAL FUENTES Juan José Aguilar Melchor ALEJANDRO FACUNDO BARRIENTOS PRIEGO Bernardo Bernal Valenzo ALVARO CASTAÑEDA VILDOZOLA FRANCISCO GUTIERREZ RODRIGUEZ Armando Ibáñez Martínez José Humberto Jiménez Martínez (2012)

En ocho capítulos se aborda el estado del arte de la vid silvestre en México

El estudio de las especies vegetales nativas de México representa un reto que cada día más investigadores mexicanos asumen. Durante muchos años, el apoyo a la investigación pública ha sido mínimo; desde el punto de vista agronómico es insuficiente para avanzar a la velocidad que requiere nuestro país para afrontar problemas de producción y distribución de alimentos. Por esa razón, entre otras, me es grato presentar esta obra que compila parte de los trabajos de la Red de Vid Silvestre patrocinada por el Sistema Nacional de Recursos Fitogenéticos (sinarefi) dependiente de la sagarpa; trabajos apuntalados por investigadores que sin pertenecer a la red han colaborado en el estudio de las plantas del género Vitis. En este libro se muestra el potencial del país para aprovechar el recurso vid, empleado desde antes de la conquista española por nativos mexicanos que conocían sus bondades. Es necesario continuar el avance en el conocimiento de este recurso, por ello el presente libro pretende invitar a toda persona interesada en contribuir con el rescate y conservación de las vides mexicanas. Los autores y editores, así como las instituciones en donde laboramos y aquellas que patrocinan estas investigaciones, esperamos se cumpla este objetivo y que el lector, alumno, profesor, investigador, público en general, disfrute esta lectura y, sobre todo, se interese en el recurso Vitis

SEP, SINAREFI, UAEMEX

Book

uva silvestre frutal nativo vitaceae parra silvestre portainjerto filoxera vitis spp CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Estudios de Caso sobre Ciencias Agropecuarias y Rurales en el siglo XXI.

ADOLFO ARMANDO RAYAS AMOR NOE ANTONIO AGUIRRE GONZALEZ ALMA INES SOTERO GARCIA ANA ABYGAYL ESTRADA LAZCANO ANA GABRIELA RINCON RUBIO ANA MEJIA CANALES ANDREA EDURNE JIMENEZ RUIZ ANGEL ROBERTO MARTINEZ CAMPOS ANGELICA MARIA DE JESUS ESPINOZA ORTEGA AURA MERCADO ORDOÑEZ BEATRIZ MATIAS GONZALEZ CARLOS GALDINO MARTINEZ GARCIA CARLOS MANUEL ARRIAGA JORDAN CARLOS RUBEN AGUILAR GOMEZ CARMEN DELIA HERNANDEZ LINARES CESAR DIAZ TALAMANTES MARIA CRISTINA CHAVEZ MEJIA DANIEL DE JESUS CONTRERAS ANGEL ROLANDO ENDARA AGRAMONT ENRIQUE ESPINOSA AYALA ERNESTO SANCHEZ VERA FABIANA SANCHEZ PLATA FRANCISCO ERNESTO MARTINEZ CASTAÑEDA FRANCISCO HERRERA TAPIA MARIA GLADYS RIVERA HERREJON ALBA GONZALEZ JACOME HUMBERTO THOME ORTIZ IDALIA VARGAS MILLAN IVONNE VIZCARRA BORDI JOSE DE LA LUZ MOTA PEREZ JULIETA GERTRUDIS ESTRADA FLORES LAURA XIMENA ESTEVEZ MORENO LAZARO BECERRA PEREZ Leidy Diana Morales Díaz LEONOR GUADALUPE DELGADILLO GUZMAN LUIS BRUNETT PEREZ MANUEL GONZALEZ RONQUILLO MARISOL FIGUEROA MEDINA MARLIN PEREZ SUAREZ NADINNE IVETTE GONZALEZ ROMERO EUFEMIO GABINO NAVA BERNAL OCTAVIO ALONSO CASTELAN ORTEGA PAOLA VILLANUEVA DIAZ Patricia Gascón Muro SERGIO MOCTEZUMA PEREZ TIZBE TERESA ARTEAGA REYES VICTOR DANIEL AVILA AKERBERG WILLIAM GOMEZ DEMETRIO (2017)

Libro científico sobre estudios de casos en el medio agropecuario y rural

Con el advenimiento del siglo XXI y el avance de los procesos de globalización, el medio rural presenta diversos cambios económicos, sociales, políticos y culturales. Lo anterior significa que el campo es un objeto de estudio altamente dinámico, complejo e inasible. las ciencias agropecuarias y rurales, en la actualidad, requieren de un abordaje sistémico e interdisciplinario que den cuenta de la heterogeneidad de situaciones y contextos que enfrenta el campo mexicano. La presente obra agrupa 18 estudios de caso, que capturan algunas fotografías de las diversas problemáticas de la ruralidad mexicana, con lo cual se pretende dar cuenta tanto de los objetivos de estudio como de la perspectiva teórico metodológico desde que estos son abordados. lo anterior tiene que ver con el hecho de que las ciencias agropecuarias y rurales manifiestan un alto grado de observación empírica, motivo por el que los estudios de caso se convierten en la perspectiva metodológica idónea que permite ir y venir de la realidad a la teoría y viceversa para la construcción de objetos de estudio. En este volumen se aborda una gran diversidad de casos, que sintetizan la heterogeneidad de enfoques y perspectivas mediante las cuales los fenómenos agropecuarios y rurales han sido abordados en el Instituto de Ciencias Agropecuarias y Rurales de la Universidad Autónoma del Estado de México, en los últimos 30 años.

Book

Estudios de Caso Ciencias Agropecuarias Ciencias Rurales Siglo XXI CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA