A new LogitBoost algorithm for multiclass unbalanced data classification

Author

Jie Song ; Xiaoling Lu ; Miao Liu ; Xizhi Wu

Author_Institution

Sch. of Stat., Capital Univ. of Econ. & Bus., Beijing, China

Volume

2

fYear

2011

fDate

26-28 July 2011

Firstpage

974

Lastpage

977

Abstract

LogitBoost algorithm is an extension of Adaboost algorithm. It replaces the exponential loss of Adaboost algorithm to conditional Bernoulli likelihood loss. LogitBoost-J algorithm further extends the LogitBoost to multiclass situation. But like LogitBoost algorithm and Adaboost algorithm, LogitBoost-J algorithm is not suitable for unbalanced data classification. This paper proposes a new LogitBoost algorithm for multiclass unbalanced data classification. The experiment on practical data shows that this new algorithm performs better than LogitBoost-J algorithm and is competitive to BABoost algorithm.

Keywords

learning (artificial intelligence); pattern classification; Adaboost algorithm; BABoost algorithm; LogitBoost-J algorithm; conditional Bernoulli likelihood loss; multiclass unbalanced data classification; Blogs; Boosting; Classification algorithms; Educational institutions; Glass; Machine learning algorithms; Prediction algorithms; LogitBoost; Multiclass; Unbalanced data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-61284-180-9

Type

conf

DOI

10.1109/FSKD.2011.6019654

Filename

6019654