Weizhong Yan

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  • Weizhong Yan
  • IEEE Transactions on Neural Networks and Learning…
  • 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 describes a method for fusing a collection of classifiers where the fusion can compensate for some positive correlation among the classifiers. 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)
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 10th International Conference on Information…
  • 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)
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)
We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our(More)
This paper presents a computer aided detection scheme on digital chest radiographs for pneumoconiosis screening. The scheme involves several medical image processing and analysis technologies, i.e. lung segmentation algorithm using the active shape model, image enhancement and features extraction from lung regions, feature down-selection by correlation(More)
1 0-7803-6599-2/01/$10.00 © 2001 IEEE Abstract—Present autonomy technologies do not compensate for a vehicle's structural, perceptual and control limitations through reflexive responses and rapid adaptation as a pilot typically does. For Uninhabited Air Vehicles (UAV's) to have more general application the mishap rates must be much reduced. Therefore the(More)