Title :
Compensating Hypothesis by Negative Data
Author :
Jiang, Fuhua ; Preethy, A.P. ; Zhang, Yan-Qing
Author_Institution :
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA
Abstract :
The properties of training data set such as size, distribution and number of attributes significantly contribute to the generalization error of a learning machine. A data set not well-distributed is prone to lead to a model with partial overfitting. The approach proposed in this paper for the binary classification enhances the useful data information by mining negative data based on the understanding of Chinese traditional Yin-Yang theory
Keywords :
data mining; learning (artificial intelligence); Chinese traditional Yin-Yang theory; binary classification; generalization error; learning machine; negative data mining; partial overfitting; Boosting; Computer errors; Computer science; Data mining; Machine learning; Machine learning algorithms; Support vector machines; Testing; Training data;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1615013
