Combine vector quantization and support vector machine for imbalanced datasets

In cases of extremely imbalanced dataset with high dimensions, standard machine learning techniques tend to be overwhelmed by the large classes. This paper rebalances skewed datasets by compressing the majority class. This approach combines Vector Quantization and Support Vector Machine and constructs a new approach, VQ-SVM, to rebalance datasets without significant information loss. Some issues, e.g. distortion and support vectors, have been discussed to address the trade-off between the information loss and undersampling. Experiments compare VQ-SVM and standard SVM on some imbalanced datasets with varied imbalance ratios, and results show that the performance of VQ-SVM is superior to SVM, especially in case of extremely imbalanced large datasets.IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other TechnologiesRed de Universidades con Carreras en Informática (RedUNCI

Combine vector quantization and support vector machine for imbalanced datasets

In cases of extremely imbalanced dataset with high dimensions, standard machine learning techniques tend to be overwhelmed by the large classes. This paper rebalances skewed datasets by compressing the majority class. This approach combines Vector Quantization and Support Vector Machine and constructs a new approach, VQ-SVM, to rebalance datasets without significant information loss. Some issues, e.g. distortion and support vectors, have been discussed to address the trade-off between the information loss and undersampling. Experiments compare VQ-SVM and standard SVM on some imbalanced datasets with varied imbalance ratios, and results show that the performance of VQ-SVM is superior to SVM, especially in case of extremely imbalanced large datasets.IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other TechnologiesRed de Universidades con Carreras en Informática (RedUNCI

Prototipo de sistema inteligente basado en patrones de ondas cerebrales para prevenir accidentes de tránsito

This article presents the prototype of an intelligent system based on patterns of brain waves to prevent traffic accidents,by which, through a sensor, placed on the driver's head, monitors the patterns of brain waves that are sent in real time via Bluetoothto a Raspberry Pi to be processed with machine learning strategies. In this way it allows to send a visual and sound warning when itdetects the state of drowsiness in the driver. For the prototype construction, data of four people were collected while they were awake,drowsy and asleep. The data set was processed with four supervised learning algorithms: nearest neighbors, support vector machine,decision trees and random forests; the last one was the one that obtained the best result, reaching 82.05% accuracy whendifferentiating the three different states. The estimated cost of the system is 210 USD, resulting an economic system in relation toothers existing in the market.Este artículo presenta el prototipo de sistema inteligente basado en patrones de ondas cerebrales para prevenir accidentesde tránsito, que, mediante un sensor colocado en la cabeza del conductor, monitoriza los patrones de ondas cerebrales los cuales sonenviados en tiempo real vía Bluetooth a una placa Raspberry Pi para ser procesados con estrategias de aprendizaje automático y deesta forma enviar una alerta visual y sonora cuando detecta el estado de somnolencia en el conductor. Para la construcción delprototipo se recogieron datos de cuatro personas en tres estados distintos, mientras estaban despiertas, somnolientas y dormidas. Elconjunto de datos fue procesado con cuatro algoritmos de aprendizaje supervisado: vecinos más cercanos, máquina de soportevectorial, árboles de decisión, bosques aleatorios; siendo este último el que mejores resultados mostró alcanzando un 82.05% deprecisión al diferenciar los tres estados anteriormente mencionados. El costo estimado del sistema es de 210 USD, resultando unsistema económico con relación a otros existentes en el mercado

Modificación del sesgo de una SVM entrenada sobre clases no balanceadas

En el área de aprendizaje automático, uno de los problemas que se presenta es el relacionado con las clases no balanceadas. Esto ocurre cuando en el conjunto de datos se dispone de muchos ejemplos de una clase, pero muy pocos de otra. La principal contribución de este trabajo es la definición de un sesgo modificado, con la SVM entrenada original, de forma que se mejora la generalización sobre conjuntos no balanceados medida en forma de media geométrica. Una ventaja importante de nuestra propuesta es que el problema de optimización para hallar la SVM no cambia para el sesgo elegido y, por tanto, el coste computacional es casi nulo. Los resultados de experimentación confirman que la propuesta iguala en prestaciones aquellas con mayor rendimiento en la literatura, mientras que no añade coste computacional.Postprint (published version

Combine vector quantization and support vector machine for imbalanced datasets

In cases of extremely imbalanced dataset with high dimensions, standard machine learning techniques tend to be overwhelmed by the large classes. This paper rebalances skewed datasets by compressing the majority class. This approach combines Vector Quantization and Support Vector Machine and constructs a new approach, VQ-SVM, to rebalance datasets without significant information loss. Some issues, e.g. distortion and support vectors, have been discussed to address the trade-off between the information loss and undersampling. Experiments compare VQ-SVM and standard SVM on some imbalanced datasets with varied imbalance ratios, and results show that the performance of VQ-SVM is superior to SVM, especially in case of extremely imbalanced large datasets. © 2006 International Federation for Information Processing