Title :
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
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;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019654
