Observations on Boosting Feature Selection

Autor:	K. Lebart, D. B. Redpath
Rok vydání:	2005
Předmět:	Boosting (machine learning) Computer science business.industry Pattern recognition Feature selection Machine learning computer.software_genre Sensor fusion BrownBoost ComputingMethodologies_PATTERNRECOGNITION AdaBoost Artificial intelligence business computer
Zdroj:	Multiple Classifier Systems ISBN: 9783540263067 Multiple Classifier Systems
DOI:	10.1007/11494683_4
Popis:	This paper presents a study of the Boosting Feature Selection (BFS) algorithm [1], a method which incorporates feature selection into Adaboost. Such an algorithm is interesting as it combines the methods studied by Boosting and ensemble feature selection researchers. Observations are made on generalisation, weighted error and error diversity to compare the algorithms performance to Adaboost while using a nearest mean base learner. Ensemble feature prominence is proposed as a stop criterion for ensemble construction. Its quality assessed using the former performance measures. BFS is found to compete with Adaboost in terms of performance, despite the reduced feature description for each base classifer. This is explained using weighted error and error diversity. Results show the proposed stop criterion to be useful for trading ensemble performance and complexity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::333e5037f448e48a6b6ded23d2c86dde https://doi.org/10.1007/11494683_4 Zobrazit plný text záznamu