A Feature Selection Model based on High-Performance Computing (HPC) Techniques

High-Performance Computing (HPC)

CBFS: High Performance Feature Selection Algorithm Based on Feature Clearness

BACKGROUND The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes. This is expected to bring reduction of processing time and improvement of classification accuracy. METHODOLOGY In this study, we devised a new feature selection algorithm (CBFS) based on clearness of features. Feature clearness exp...

HPC-ICTM: The Interval Categorizer Tessellation-Based Model for High Performance Computing

This paper presents the Interval Categorizer Tessellationbased Model (ICTM) for the simultaneous categorization of geographic regions considering several characteristics (e.g., relief, vegetation, land use etc.). Interval techniques are used for the modelling of uncertain data and the control of discretization errors. HPC-ICTM is an implementation of the model for clusters. We analyze the perfo...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

High Performance Grid Computing: getting HPC and HTC all together