1,334 research outputs found
An approach for improved studentsâ performance prediction using homogeneous and heterogeneous ensemble methods
Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict studentsâ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naĂŻve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict studentsâ performance. The comparative analysis of the modelâs accuracy showed that the best-performing single classifierâs accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods
An approach for improved studentsâ performance prediction using homogeneous and heterogeneous ensemble methods
Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict studentsâ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naĂŻve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict studentsâ performance. The comparative analysis of the modelâs accuracy showed that the best-performing single classifierâs accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods
Ensemble learning in the presence of noise
Tesis Doctoral inĂ©dita leĂda en la Universidad AutĂłnoma de Madrid, Escuela PolitĂ©cnica Superior, Departamento de Ingenieria InformĂĄtica. Fecha de lectura: 14-02-2019La disponibilidad de grandes cantidades de datos provenientes de diversas fuentes ampl a
enormemente las posibilidades para una explotaci on inteligente de la informaci on. No
obstante, la extracci on de conocimiento a partir de datos en bruto es una tarea compleja
que requiere el desarrollo de m etodos de aprendizaje e cientes y robustos. Una de las
principales di cultades en el aprendizaje autom atico es la presencia de ruido en los datos.
En esta tesis, abordamos el problema del aprendizaje autom atico en presencia de ruido.
Para este prop osito, nos centraremos en el uso de conjuntos de clasi cadores. Nuestro
objetivo es crear colecciones de aprendices base cuyos resultados, al ser combinados,
mejoren no solo la precisi on sino tambi en la robustez de las predicciones.
Una primera contribuci on de esta tesis es aprovechar el ratio de submuestreo para construir
conjuntos de clasi cadores basados en bootstrap (como bagging o random forests)
precisos y robustos. La idea de utilizar el submuestreo como mecanismo de regularizaci
on tambi en se explota para la detecci on de ejemplos ruidosos. En concreto, los
ejemplos que est an mal clasi cados por una fracci on de los miembros del conjunto se
marcan como ruido. El valor optimo de este umbral se determina mediante validaci on
cruzada. Las instancias ruidosas se eliminan ( ltrado) o se corrigen sus etiquetas de su
clase (limpieza). Finalmente, se construye un conjunto de clasi cadores utilizando los
datos de entrenamiento limpios ( ltrados o limpiados).
Otra contribuci on de esta tesis es vote-boosting, un m etodo de conjuntos secuencial
especialmente dise~nado para ser robusto al ruido en las etiquetas de clase. Vote-boosting
reduce la excesiva sensibilidad a este tipo de ruido de los algoritmos basados en boosting,
como adaboost. En general, los algoritmos basados en booting modi can la distribuci on
de pesos en los datos de entrenamiento progresivamente para enfatizar instancias mal
clasi cadas. Este enfoque codicioso puede terminar dando un peso excesivamente alto
a instancias cuya etiqueta de clase sea incorrecta. Por el contrario, en vote-boosting, el
enfasis se basa en el nivel de incertidumbre (acuerdo o desacuerdo) de la predicci on del
conjunto, independientemente de la etiqueta de clase. Al igual que en boosting, voteboosting
se puede analizar como una optimizaci on de descenso por gradiente en espacio
funcional.
Uno de los problemas abiertos en el aprendizaje de conjuntos es c omo construir combinaciones
de clasi cadores fuertes. La principal di cultad es lograr diversidad entre
los clasi cadores base sin un deterioro signi cativo de su rendimiento y sin aumentar
en exceso el coste computacional. En esta tesis, proponemos construir conjuntos de
SVM con la ayuda de mecanismos de aleatorizaci on y optimizaci on. Gracias a esta combinaci on de estrategias complementarias, es posible crear conjuntos de SVM que
son mucho m as r apidos de entrenar y son potencialmente m as precisos que un SVM
individual optimizado.
Por ultimo, hemos desarrollado un procedimiento para construir conjuntos heterog eneos
que interpolan sus decisiones a partir de conjuntos homog eneos compuestos por diferentes
tipos de clasi cadores. La composici on optima del conjunto se determina mediante
validaci on cruzada.
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Open-source neural architecture search with ensemble and pre-trained networks
The training and optimization of neural networks,
using pre-trained, super learner and ensemble approaches is
explored. Neural networks, and in particular Convolutional
Neural Networks (CNNs), are often optimized using default
parameters. Neural Architecture Search (NAS) enables
multiple architectures to be evaluated prior to selection of the
optimal architecture. Our contribution is to develop, and make
available to the community, a system that integrates open
source tools for the neural architecture search (OpenNAS) of
image classification models. OpenNAS takes any dataset of
grayscale, or RGB images, and generates the optimal CNN
architecture. Particle Swarm Optimization (PSO), Ant Colony
Optimization (ACO) and pre-trained models serve as base
learners for ensembles. Meta learner algorithms are
subsequently applied to these base learners and the ensemble
performance on image classification problems is evaluated. Our
results show that a stacked generalization ensemble of
heterogeneous models is the most effective approach to image
classification within OpenNAS
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