Khalil El Hindi is a professor, at the Department of Computer Science, King Saud University. His main research interests include machine learning, classification algorithms, outlier detection, instance weighing, ensembles of classifiers, similarity distance metrics, and Neural Networks.
Enhancing distance measures is the key to improve the performance of instance-based learning (IBL) and many machine learning (ML) algorithms. The value difference metrics (VDM) and inverted...
Web Service Composition (WSC) aims to select and aggregate
many web services to generate a work ow. The workflow contains many tasks
and for each task there are many web services to choose...
The Naive Bayes (NB) learning algorithm is simple and effective in many domains including text classification. However, its performance depends on the accuracy of the estimated conditional...