Italiano (Italian) English (Inglese)
venerdì, 15 dicembre 2017

Pubblicazioni

Indietro
Dettagli Pubblicazione
Autori:Luigi Portinale
Lorenza Saitta
Area Scientifica:Artificial Intelligence
Machine Learning
Titolo:Feature Selection
Apparso su:Deliverable D14.1, EU Funded Project MINIG MART
Anno:2002
Tipo Pubblicazione:Other
URL:http://www-ai.cs.uni-do.../portinale_saitta_2002a.pdf
Sommario:The problem of feature selection is fundamental in a number of different tasks like classification, data mining, image processing, conceptual learning, etc. . . In recent times, the growing inportance of knowledge discovery and data-mining approaches in practical applications has made the feature selection problem a quite hot topic, especially when considering the mining of knowledge from real-world databases or warehouses, containing not only a huge amount of records, but also a significant number of features not always relevant for the task at hand. Looking at the literature, there are essentially two main fields where the feature selection problem has been extensively studied: Statistical Pattern Recognition, Machine Learning. In the first field, feature selection is considered from the classification point of view, i.e. the problem is approached having the construction of an efficient classifier (i.e. a pattern recognizer) as a target. In the Machine Learning community, more emphasis is given to the more general problem of concept learning, even if classification still remain an important issue. However, this difference has provided different approaches aimed at solving the problem, addressing different perspectives and points of view. In the paPER, we will report on the main proposals and approches developed inside the two communities.