Chun-Chin Hsu

Learn More
Recently, the class imbalance problem has attracted much attention from researchers in the field of data mining. When learning from imbalanced data in which most examples are labeled as one class and only few belong to another class, traditional data mining approaches do not have a good ability to predict the crucial minority instances. Unfortunately, many(More)
The Tennessee Eastman (TE) process, created by Eastman Chemical Company, is a complex nonlinear process. Many previous studies focus on the detectability of monitoring a multivariate process by using TE process as an example. Principal component analysis (PCA) is a widely used dimension-reduction tool for monitoring multivariate linear process. Recently,(More)
In order to ensure power quality and keep supplying power in a thermal power plant, early detection of equipment malfunctions is a critical issue. This study attempts to develop an adaptive forecast-based chart so as to enhance the fault detectability in a thermal power plant. In the proposed monitoring statistic, the exponentially weighted moving average(More)
Lots of real-world data sets have imbalanced class distributions in which almost all examples belong to one class and far fewer instances belong to others. Compared with the majority examples, the minority examples are usually more interesting class, such as rare diseases in diagnosis data, failures in inspection data, frauds in credit screening data, and(More)
Not alike to principal component analysis (PCA) based monitoring statistics (T<sup>2</sup> and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I<sup>2</sup> , I<sub>e</sub> <sup>2</sup> and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian(More)