Convex Hull Ensemble Machine

Author

Kim, Yongdai

Author_Institution

Dept. of Stat., Ewha Woman´´s Univ., Seoul, South Korea

fYear

2002

fDate

2002

Firstpage

243

Lastpage

249

Abstract

We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is developed first and it is modified to regression and classification problems. Empirical studies show that in classification problems CHEM has similar prediction accuracy as AdaBoost, but CHEM is much more robust to output noise. In regression problems, CHEM works competitively with other ensemble methods such as Gradient Boost and Bagging.

Keywords

Hilbert spaces; data mining; decision trees; learning (artificial intelligence); pattern classification; statistical analysis; AdaBoost; Bagging; CHEM; Convex Hull Ensemble Machine; Gradient Boost; Hilbert space; classification; data mining; decision trees; ensemble algorithm; machine learning; output noise; regression; Accuracy; Bagging; Decision trees; Geometry; Hilbert space; Machine learning; Machine learning algorithms; Noise robustness; Solid modeling; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on

Print_ISBN

0-7695-1754-4

Type

conf

DOI

10.1109/ICDM.2002.1183909

Filename

1183909

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3249208