Feature Selection based on Information Theory
Year: 2010
Type: dissertation
Abstract: Along with the improvement of data acquisition techniques and the increasing computational capacity of computers, the dimensionality of the data grows higher. Pattern recognition methods have to deal with samples consisting of thousands of features and the reduction of their dimensionality becomes crucial to make them tractable. Feature selection is a technique for removing the irrelevant and noisy features and selecting a subset of features which describe better the samples and produce a better classification performance. It is becoming an essential part of most pattern recognition applications.
Author: Boyán Bonev
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1761159547