Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for bin...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.04.054 * Corresponding author. E-mail addresses: [email protected] (D. Zhan Zhou), [email protected] (S.C.H. Leung). In recent years, more and more people, especially young people, begin to use credit card with the changing of consumption concept in China so that the business on credit cards is growing fast. The...

The present experiment was conducted to study the effect of pre-harvest bagging and spray of CaCl2 and K2SO4 on quality and shelf life of mango fruits cv. Amrapali during two succeeding years. Trees of Amrapali mango were sprayed three times at 30, 20, and10 days before harvesting and bagging with brown paper bag 20 days before harvesting of fruits. Harvested fruits were stored under the ambien...

For class imbalance problem, the integration of sampling and ensemble methods has shown great success among various methods. Nevertheless, as the representatives of sampling methods, undersampling and oversampling cannot outperform each other. That is, undersampling fits some data sets while oversampling fits some other. Besides, the sampling rate also significantly influences the performance o...

Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing metho...

In this paper, we compare the performances of classifier combination methods (bagging, modified random subspace method, classifier selection, parametric fusion) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are: (a) combination function among input variables, (b) correlation between input variables, (c) varianc...