Activity Graph Feature Selection for Activity Pattern Classification

Discriminative Feature Selection for Uncertain Graph Classification

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presenc...

Adaptive Sequential Feature Selection for Pattern Classification

Feature selection helps to focus resources on relevant dimensions of input data. Usually, reducing the input dimensionality to the most informative features also simplifies subsequent tasks, such as classification. This is, for instance, important for systems operating in online mode under time constraints. However, when the training data is of limited size, it becomes difficult to define a sin...

A fuzzy neural network for pattern classification and feature selection

Particle Swarm Feature Selection for fMRI Pattern Classification

The application of pattern recognition to functional magnetic resonance imaging (fMRI) data enables exiting possibilities, including mind-reading and brain-machine interfacing. This paper presents a novel brain state identification approach, which, using an algorithm based on particle swarm optimization (PSO) in conjunction with a classifier of choice, identifies important brain voxels – thus b...

Feature Selection for Classification

Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the 1970’s to the present. It identifies four s...