For small samples, classi er design algorithms typically suffer from over tting. Given a set of features, a classi er must be designed and its error estimated. For small samples, ...
Seungchan Kim, Edward R. Dougherty, Junior Barrera...
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classi er learning systems. Both form a set of classi ers t...
The automatic construction of classi ers programs able to correctly classify data collected from the real world is one of the major problems in pattern recognition and in a wide ar...
Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bell...
This paper presents a generic features selection method and its applications on some document analysis problems. The method is based on a genetic algorithm (GA), whose tness funct...
We analyze critically the use of classi cation accuracy to compare classi ers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learnin...
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...