Abstract
ABSTRACTThis article presents an approach to simplify pattern recognition problems via a scatter search algorithm that is applied as feature subset selector (FSS). Experimentation on five high-dimensionality problems, with a feature space in the range 2308–16063 and feature-to-pattern ratios greater than 27, revealed that the most appropriate feature selector is based on correlation. Moreover, the most accurate way is to combine the new proposal with a correlation-based attribute evaluator with a Naive Bayes Tree classifier; their performance has been compared with a reference FSS and sheds light on very interesting results in terms of accuracy and problem reduction.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
