Wenjin Lu

The KGP model of agency for global computing: computational model and prototype implementation

We present the computational counterpart of the KGP (Knowledge, Goals, Plan) declarative model of agency for Global Computing. In this context, a computational entity is seen as an agent developed using Computational Logic tools and techniques. We model a KGP agent by relying upon a collection of capabilities, which are then used to define a collection of transitions, to be used within logically specified, context sensitive control theories, which we call cycle theories. In close relationship to the declarative model, the computational model mirrors the logical architecture by specifying appropriate computational counterparts for the capabilities and using these to give the computational models of the transitions. These computational models and the one specified for the cycle theories are all based on, and are significant extensions of, existing proof procedures for abductive logic programming and logic programming with priorities. We also discuss a prototype implementation of the overall computational model for KGP.

One-class kernel subspace ensemble for medical image classification

Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles

Accurate and reliable classification of microscopic biopsy images is an important issue in computer assisted breast cancer diagnosis. In this paper, a new cascade Random Subspace ensembles scheme with reject options is proposed for microscopic biopsy image classification. The classification system is built as a serial fusion of two different Random Subspace classifier ensembles with rejection options to enhance the classification reliability. The first ensemble consists of a set of Support Vector Machine classifiers that converts the original

Highly reliable breast cancer diagnosis with cascaded ensemble classifiers

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Competent agents and customising protocols

-class classification problem into a number of

Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology

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