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. 
Text classification is one domain in which the naive Bayesian (NB) learning algorithm performs remarkably well. However, making further improvement in performance using ensemble-building...
Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set...
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...