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The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifiers: Naïve Bayes, Tree-Augmented Naïve Bayes and a general Bayesian network. All of these are implemented using the K2 framework of Cooper… (More)

This paper introduces a new Bayesian network structure, named a Partial Bayesian Network (PBN), and describes an algorithm for constructing it. The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node. Initial experiments have compared the performance of the… (More)

We consider a dynamic market-place of self-interested agents with differing capabilities. A task to be completed is proposed to the agent population. An agent attempts to form a coalition of agents to perform the task. Before proposing a coalition, the agent must determine the optimal set of agents with whom to enter into a coalition for this task; we refer… (More)

The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a… (More)

This paper describes an extension to reinforcement learning (RL), in which a standard RL algorithm is augmented with a mechanism for transferring experience gained in one problem to new but related problems. In this approach, named Progressive RL, an agent acquires experience of operating in a simple environment through experimentation, and then engages in… (More)

The classification of high dimensional data, such as images, gene-expression data and spectral data, poses an interesting challenge to machine learning , as the presence of high numbers of redundant or highly correlated attributes can seriously degrade classification accuracy. This paper investigates the use of Principal Component Analysis (PCA) to reduce… (More)

The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels… (More)

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many… (More)