One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

Fusion and ensemble is important technique of machine learning. Fusion fused the feature attribute of different classifier and improved the classification of binary classifier. Instead of that ensemble technique provide the facility of merge two individual classifier and improve the performance of both classifiers. The ensemble technique of classifier depends on number of nearer point of classi...

In this paper we proposed a method of optimal selection of cluster for cluster oriented classifier. The cluster oriented classifier is great advantage over binary and conventional classifier. The cluster oriented classifier work very efficiently on real and sample data. But the cluster oriented ensemble classifier faced a problem of selection of number of cluster for ensemble. In current fashio...

We present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital, when reliable acquisition of vital-sign data may be difficult. The decision tool uses basic vital-sign variables as input into linear classifiers, which are then combined into an ensemble classifier. The classifier identifies hypovolemic patient...