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Topcoat paint is mainly composed of resin and pigment and hence its quality highly depends on the type and proportion of these two ingredients. This study aims at testing the formula of the topcoat paint for finding one that can achieve better quality for anti-aging. Various formulas of paint are applied on boards that will be put into ultraviolet accelerated test machines to simulate weathering tests. The gloss and color, before and after the tests, are collected and numerical prediction method M5P is used to grow model trees for discovering the key factors affecting aging. Based on the structure and the linear regression models in the trees, a better topcoat paint should be composed of a high proportion of resin and generally a low proportion of pigment. Good types of resin and pigment are also identified for keeping color and gloss.

Credit evaluation of customers is a critical issue in financial organizations. Classification algorithms have beenproposed for credit evaluation in recent years, and the class distribution in the financial data for those studies are not skewed. However, only a small proportion of customers will be the cases for bad credit. Financial records should be considered as an imbalanced data set for analyzing credit risk. Ensemble algorithms that make predictions by group decisions generally have relatively high accuracy than the ones inducing only one model from data. This study introduces a mechanism based on the weighted random forest to improve the prediction accuracy on the records with bad credit. This mechanism is tested on two financial data sets to demonstrate that it can achieve relatively high performance in evaluating credit risk and that the number of decision trees in a forest is not helpful. Critical attributes are also identified to provide practical meanings for credit risk analysis.