Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming

Khan, Maryam Mahsal; Mendes, Alexandre; Zhang, Ping; Chalup, Stephan K.

doi:10.1016/j.neucom.2017.03.048

Cited by 26 publications

(13 citation statements)

References 78 publications

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“…Various other types of ANNs have been evolved using CGP including convolutional ANNs [99] and wavelet ANNs [47,50]. There is now a growing literature on CGPANNs and they have been applied to many other applications including financial [130],medical [1][2][3][4], client prediction [5], load forecasting [41,42,45], internet traffic estimation [44] and signal reconstruction [51].…”

Section: Applicationsmentioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are wellknown to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.

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Section: Applicationsmentioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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“…GAs have been developed and applied for biomarker profile identification in a range of settings including Alzheimer's disease progression and breast cancer diagnosis [21,22]. The GA algorithms have also been modified and improved to adapt to different computational environments and for different applications [23,24]. An application of GA for for selecting vaginal microbiome features associated with bacterial vaginosis was found in [25].…”

Section: B Genetic Algorithmsmentioning

confidence: 99%

Combination of Principal Component Analysis and Genetic Algorithm for Microbial Biomarker Identification in Obesity

Zhang

West

Chen

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background: A large number of microbial species have been detected in human faecal samples, with many of the species having high correlations with each other. Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to see whether individuals can be clustered into two classification groups based on predetermined criteria: control and disease group. However, there might be a combination of other components that better distinguish diseased individuals from healthy controls. Genetic algorithms (GA) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and GA for identifying sets of bacterial species associated with high body mass. Results: GA has selected the subsets of the principal components (PCs) produced by PCA. The prediction models built with theses PCs produced much higher area under the curve (AUC) values compared to the models built using topPCs which explained the most variance in the sample. The combinatorial effect of the identified bacterial species that contributed the most to the PCs may be associated with body mass. Conclusions:The proposed algorithm overcomes the limitation of using PCA for prediction modelling. The application of the algorithm on an obesity study has shown the value of applying GA for selecting PC subsets from PCA to improve prediction models. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

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“…Los involucrados en este proyecto son el Hospital General de Massachusetts, los Laboratorios Nacionales de Sandia y el Departamento de Ciencias de la Computación e Ingeniería de la Universidad del Sur de Florida. Cuenta con 2.500 diagnósticos donde muestran ambos senos debidamente ordenados con los datos del paciente: edad al momento del estudio, clasificación de la densidad mamaria ACR, calificación de su utilidad para las anormalidades (Khan, Mendes, Zhang, & Chalup, 2017).…”

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Revisión de esquemas de clasificación de tejidos cancerígenos en imágenes de mamografía

et al. 2018

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RESUMENLa presente revisión de esquemas de clasificación analiza los algoritmos y métodos más utilizados dentro de la detección automática y clasificación supervisada de anomalías en imágenes de mamografías. Para ello, se propone el análisis de 36 artículos científicos en base a su metodología de clasificación, considerando además que estas publicaciones se encuentran indexadas en revistas reconocidas en los últimos cinco años. El estudio se ha dividido en cuatro etapas. La primera etapa es el preprocesamiento, que incluye las bases de datos utilizadas para entrenamiento y pruebas. Además, describimos los métodos de filtrados como la ecualización de histogramas (escala de grises) y el filtro Gabor, entre otros métodos que han brindado resultados eficientes. La etapa de los algoritmos de extracción de características presenta coincidencias en la utilización de la transformada discreta de coseno y la transformada discreta de ondícula entre otros algoritmos que se han utilizado para esta tarea. Para los algoritmos de clasificación, se resalta la utilización de las máquinas de vectores de soporte. En adición a la revisión de expresiones matemáticas desarrolladas en esta investigación, una de las contribuciones de este trabajo reside en proporcionar una lista de clasificadores híbridos, que resultan de la combinación de los clasificadores convencionales. Finalmente, se describen las métricas de evaluación como la curva receptora de características operativas y la precisión, que son indicadores cuantitativos del desempeño de los algoritmos revisados en este trabajo investigativo.PALABRAS CLAVE: Algoritmo de clasificación, cáncer de mama, extracción de características, mamografía, reconocimiento de patrones. ABSTRACTThe present revision of classification schemes analyzes the algorithms and methods most used within the automatic detection and supervised classification of abnormalities in mammography images. To do this, we propose the analysis of 36 scientific papers based on their classification methodology, considering also that these publications are indexed in recognized journals in the last five years. The study has been divided into four stages. The first stage is preprocessing, which includes databases used for training and testing. In addition, we describe the filtering methods such as histogram equalization (grayscale) and the Gabor filter, among other methods that have yielded efficient results. The stage of feature extraction algorithms presents coincidences in the use of the discrete cosine transform and the discrete wavelet transform, among other algorithms that have been used for this task. For the classification algorithms, we highlight the use of the support vector machine. In addition to the review of mathematical expressions developed in this research, one of the contributions of this paper is to provide a list of hybrid classifiers, which result from the combination of conventional classifiers. Finally, we describe the evaluation metrics such as the receiver operating characteristic curve and the precis...

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Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming

Cited by 26 publications

References 78 publications

Cartesian genetic programming: its status and future

Cartesian genetic programming: its status and future

Combination of Principal Component Analysis and Genetic Algorithm for Microbial Biomarker Identification in Obesity

Revisión de esquemas de clasificación de tejidos cancerígenos en imágenes de mamografía

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