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  • Weizhong Yan
  • 2012
Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network(More)
This paper investigates the use of the ρ-correlation as a measure for classifier diversity to aid in the choice of classifiers for a fusion ensemble. Specifically, we define a measure that captures the correlation for n classifiers for binary output as well as for classifier with continuous output. We then suggest the use of the ρ-correlation in classifier(More)
  • Weizhong Yan
  • 2007
Feature ranking, due to its simplicity and computational efficiency, is a widely used dimensionality reduction technique, especially for large dataset where other methods are computationally too expensive. Conventionally feature ranking is done based on a single ranking criterion. One drawback associated with the conventional, single-criterion ranking is(More)
Locomotives are complex electromechanical systems. Continuously monitoring the health state of locomotives is critical in modern cost-effective maintenance strategy. A typical locomotive is equipped with the capability to monitor their state and generate fault messages and a snapshot of sensed parametric readings in response to anomalous conditions. In our(More)
This paper describes a method for fusing a collection of classifiers where the fusion can compensate for some positive correlation among the clas-sifiers. Specifically, it does not require the assumption of evidential independence of the classifiers to be fused (such as Dempster Shafer's fusion rule). The proposed method is associative, which allows fusing(More)
Both experimental and theoretical studies have proved that classifier fusion can be effective in improving overall classification performance. Classifier fusion can be performed on either score (raw classifier outputs) level or decision level. While tremendous research interests have been on score-level fusion, research work for decision-level fusion is(More)
Classification requirements for real-world classification problems are often constrained by a given true positive or false positive rate to ensure that the classification error for the most important class is within a desired limit. For a sufficiently high true positive rate, this may result in the set-point being located somewhere in the flat portion of(More)
The tautomerization equilibria of 3-hydroxy-2-mercaptopyridine (HMP) and 2,3-dihydroxypyridine (DHP) in vacuo and in ethanol solution have been studied using the density functional theory (DFT) at B3LYP/6-31Gd level. The results indicate that the thione form of HMP and the keto form of DHP are the most stable tautomers in the equilibrium, and the energy(More)