Cross Split Decision Trees for pattern classification

Mirzamomen, Zahra; Fekri, Mohammad Navid; Kangavari, Mohammad Reza

doi:10.1109/iccke.2015.7365834

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…In the graphical interface of the expert system, all the faults that the drilling equipment notification system has will be displayed on buttons so that the user can select one and begin the technical assistance of the software. The inference engine is responsible to build the binary tree and then the path to follow, so that when entering the first node we find a Boolean condition, with two possible options (yes or no), where half of the possibilities are discarded at each level, until reaching the leaves of the tree where the possible solution to the operational incidence is found [20] . If the fault is, Loss of communication of the J box with PCR, its searches in the database for that fault to build the root of the tree (Fig 2).…”

Section: Resultsmentioning

confidence: 99%

Using an expert system for the solution of operational failures in sludge pumps in oil well drilling equipment

J¹,

M²,

C³

et al. 2022

Int. J. Eng. Comput. Sci.

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It has been developed and we proposed to implement an expert system that acts as a permanent technical assistant that contains a knowledge base, a bank of solutions and a history of failures, which allows to solve the failures that occur in the sludge pumps, when the machinery, that form the drilling equipment, is working. We estimated that the system can reduce the downtime, caused by failure, using the advantages offered by this branch of artificial intelligence that simulates the process of learning, memorization, reasoning, communication and action to solve problems; binary trees being the main data structure that was implemented. We estimate that the training time, for the handling of the expert system, is near to eight hours for personnel related to sludge pumps. In particular, the solution of a fault such as the loss of communication with the J box, the expert system would be solved in approximately 60min, while in the traditional way it could take up to four hours, depending on the availability of the specialist in charge of the drilling area. Therefore, the saving in solution time, restart and, therefore, economic resources is highly significant.

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Section: Resultsmentioning

confidence: 99%

Using an expert system for the solution of operational failures in sludge pumps in oil well drilling equipment

J¹,

M²,

C³

et al. 2022

Int. J. Eng. Comput. Sci.

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“…Así, el diagrama en forma de árbol exhibe la estructura de una decisión particular, además de su correlación conotras posibles opciones y decisiones, considerándolos entonces como clasificadores. Dichos clasificadores se componen de nodos prueba, donde se toman las decisiones, y nodos hoja, donde se expresa el valor final más acertado alcanzado mediante el flujo de decisión.Debido a que dividen el espacio de instancias (casos), de manera recursiva, en pequeños conjuntos o clases de acuerdo a un criterio de discriminación[21] , suponen una gran ayuda para la evaluación termográfica e integral de las patologías mamarias. Por lo tanto, se han realizado intentos por mejorar la clasificación térmica diagnóstica haciendo uso de ellos, como los trabajos realizados en[22,23,24] .Los árboles de decisión poseen ventajas sobre las redes neuronales en cuanto a requerimientos en la capacidad de cómputo y al tiempo de generación de resultados.…”

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Clinical decision support system for decision making in mammary glands thermography

Bastida

Cabello

Rodríguez

et al. 2017

Rev. Mex. Ing. Biomed.

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RESUMENSe desarrolló un sistema basado en árboles de decisión con factores de peso heurísticos orientados al diagnóstico mediante termografía mamaria. El sistema de apoyo a la toma de decisiones clínicas propuesto incluye árboles de decisión binarios para la clasificación estadística de algunas patologías de la glándula mamaria. Dentro de ellos, se incluyen factores de peso constantes encontrados a través de la correlación de las variables involucradas en el desarrollo de los padecimientos mamarios de acuerdo al apego estadístico de las características termográficas, así como de la información clínica complementaria (historial clínico, parámetros físicos, carga genética y hábitos, entre otros), con respecto a su incidencia diagnóstica. La certeza del sistema para los trastornos inflamatorios es de 96 %, para mastopatía fibroquística de 78 %, para necrosis grasa de 62 % y para cáncer de seno de 86 %. El 14 % de error se asocia a microcalcificaciones mismas que con la técnica termográfica reportada no es posible diferenciar. Por lo tanto, es posible catalogar el riesgo de una paciente a padecer alguna afección según los resultados arrojados después de llevar a cabo el examen térmico. Con este sistema de apoyo a la decisión se mejora la posibilidad de aplicación de pruebas de detección temprana en la población que sufre de contraindicaciones para realizarse los exámenes con otros métodos, además permite un diagnóstico integral para las demás pacientes.PALABRAS CLAVE: Sistemas de apoyo para toma de decisiones clínicas (CDSS), árboles de decisión, factores de peso, cáncer de seno, termografía. ABSTRACTA decision tree based system with heuristic weight factors oriented to diagnosis by thermography was developed. The proposed clinical decision support system (CDSS) includes binary decision trees for statistical classification of some diseases of the mammary gland. These include weight constant factors found by the correlation of the variables involved in the development of breast disorders according to the statistical attachment of the thermal features, as well as complementary clinical information (medical history, physical parameters, genetic load and habits, among others), regarding its diagnostic incidence. The certainty of the system for inflammatory disorders is 96%, for fibrocystic mastopathy is 78 %, for fat necrosis is 62% and for breast cancer is 86%. The 14% error is associated to microcalcifications that with the reported thermographic technique it is not possible to differentiate. Thus, it is possible to catalog the risk of a patient having a condition according to the results obtained after conducting a thermal test. Then, with the CDSS the possibility of applying early detection tests in the population suffering from contraindications to perform the others is improved, in addition it allows a comprehensive diagnosis for other patients.

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“…Debido a que dividen el espacio de instancias (casos), de manera recursiva, en pequeños conjuntos o clases de acuerdo a un criterio de discriminación [21] , suponen una gran ayuda para la evaluación termográfica e integral de las patologías mamarias. Por lo tanto, se han realizado intentos por mejorar la clasificación térmica diagnóstica haciendo uso de ellos, como los trabajos realizados en [22,23,24] .…”

Section: áRboles De Decisiónunclassified

Clinical decision support system for decision making in mammary glands thermography

Bastida¹

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A decision tree based system with heuristic weight factors oriented to diagnosis by thermography was developed. The proposed clinical decision support system (CDSS) includes binary decision trees for statistical classification of some diseases of the mammary gland. These include weight constant factors found by the correlation of the variables involved in the development of breast disorders according to the statistical attachment of the thermal features, as well as complementary clinical information (medical history, physical parameters, genetic load and habits, among others), regarding its diagnostic incidence. The certainty of the system for inflammatory disorders is 96%, for fibrocystic mastopathy is 78 %, for fat necrosis is 62% and for breast cancer is 86%. The 14% error is associated to microcalcifications that with the reported thermographic technique it is not possible to differentiate. Thus, it is possible to catalog the risk of a patient having a condition according to the results obtained after conducting a thermal test. Then, with the CDSS the possibility of applying early detection tests in the population suffering from contraindications to perform the others is improved, in addition it allows a comprehensive diagnosis for other patients

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Cross Split Decision Trees for pattern classification

Cited by 4 publications

References 21 publications

Using an expert system for the solution of operational failures in sludge pumps in oil well drilling equipment

Using an expert system for the solution of operational failures in sludge pumps in oil well drilling equipment

Clinical decision support system for decision making in mammary glands thermography

Clinical decision support system for decision making in mammary glands thermography

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