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Neural networks for classification: a survey
 and Cybernetics  Part C: Applications and Reviews
, 2000
"... Abstract—Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability esti ..."
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Abstract—Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics. Index Terms—Bayesian classifier, classification, ensemble methods, feature variable selection, learning and generalization, misclassification costs, neural networks. I.
Understanding Neural Networks as Statistical Tools
 The American Statistician
, 1996
"... Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification; areas where regression models and other related statistical techniques have traditionally been used. In this paper, we discuss neural networks and compa ..."
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Cited by 29 (0 self)
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Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification; areas where regression models and other related statistical techniques have traditionally been used. In this paper, we discuss neural networks and compare them to regression models. We start by exploring the history of neural networks. This includes a review of relevant literature on the topic of neural networks. Neural network nomenclature is then introduced and the backpropagation algorithm, the most widely used learning algorithm, is derived and explained in detail. A comparison between regression analysis and neural networks in terms of notation and implementation is conducted to aid the reader in understanding neural networks. We compare the performance of regression analysis with that of neural networks on two simulated examples and one example on a large data set. We show that neural networks act as a type of nonparametric regression...
Extracting Rules From Pruned Neural Networks for Breast Cancer Diagnosis
 Artificial Intelligence in Medicine
, 1996
"... A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of hid ..."
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Cited by 28 (3 self)
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A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of hidden unit activation values. The accuracy of the extracted rules is as high as the accuracy of the pruned network. For the breast cancer diagnosis problem, the concise rules extracted from the network achieve an accuracy rate of more than 95 % on the training data set and on the test data set. Keywords. Neural network pruning; penalty function; rule extraction; breast cancer diagnosis. 2 1 Introduction Neural networks techniques have recently been applied to many medical diagnostic problems [1, 2, 4, 5, 11, 22]. Although the predictive accuracy of neural networks is often higher than that of other methods or human experts, it is generally difficult to understand how the network arrives a...
Adapting an Ensemble Approach for the Diagnosis of Breast Cancer
 in Proceedings of ICANN 98
, 1998
"... this paper, we describe an adaptation of the ensemble approach, in an effort to meet the constraints of medical diagnosis. A neural net ensemble consists of a committee of several nets, where the several outputs to each input are combined in some fashion (e.g. simple averaging). It can be contrasted ..."
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Cited by 14 (1 self)
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this paper, we describe an adaptation of the ensemble approach, in an effort to meet the constraints of medical diagnosis. A neural net ensemble consists of a committee of several nets, where the several outputs to each input are combined in some fashion (e.g. simple averaging). It can be contrasted with the traditional approach of choosing the best performing net. It has been shown e.g. [1] that the combined output from an ensemble can result in improved generalisation as compared to the selection and use of the best performing single net. However, it is necessary to adapt this approach before applying it to most medical applications.
Robust Full Bayesian Learning for Neural Networks
, 1999
"... In this paper, we propose a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We develop a reversible jump Markov chain Monte ..."
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Cited by 12 (9 self)
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In this paper, we propose a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We develop a reversible jump Markov chain Monte Carlo (MCMC) method to perform the necessary computations. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible jump MCMC simulated annealing algorithm to optimise neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We show that by calibrating the full hierarchical ...
Neural Expert Systems
, 1995
"... The advantages and disadvantages of classical rulebased and neural approaches to expert system design are complementary. We propose a strictly neural expert system architecture, that enables the creation of the knowledge base automatically, by learning from example inferences. For this purpose we e ..."
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Cited by 12 (2 self)
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The advantages and disadvantages of classical rulebased and neural approaches to expert system design are complementary. We propose a strictly neural expert system architecture, that enables the creation of the knowledge base automatically, by learning from example inferences. For this purpose we employ a multilayered neural network, trained with generalized back propagation for interval training patterns, that also makes the learning of patterns with irrelevant inputs and outputs possible. We eliminate the disadvantages of the neural approach by enriching the system with the heuristics to work with incomplete information, and to explain the conclusions. The structure of the expert attributes is optional, and a user of the system can define the types of inputs and outputs (real, integer, scalar type, and set), and the manner of their coding (floating point, binary, and unary codes). We have tested our neural expert system on several nontrivial realworld problems (e.g., the diagnost...
Bayesian Methods for Neural Networks
, 1999
"... Summary The application of the Bayesian learning paradigm to neural networks results in a flexible and powerful nonlinear modelling framework that can be used for regression, density estimation, prediction and classification. Within this framework, all sources of uncertainty are expressed and meas ..."
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Cited by 10 (0 self)
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Summary The application of the Bayesian learning paradigm to neural networks results in a flexible and powerful nonlinear modelling framework that can be used for regression, density estimation, prediction and classification. Within this framework, all sources of uncertainty are expressed and measured by probabilities. This formulation allows for a probabilistic treatment of our a priori knowledge, domain specific knowledge, model selection schemes, parameter estimation methods and noise estimation techniques. Many researchers have contributed towards the development of the Bayesian learning approach for neural networks. This thesis advances this research by proposing several novel extensions in the areas of sequential learning, model selection, optimisation and convergence assessment. The first contribution is a regularisation strategy for sequential learning based on extended Kalman filtering and noise estimation via evidence maximisation. Using the expectation maximisation (EM) algorithm, a similar algorithm is derived for batch learning. Much of the thesis is, however, devoted to Monte Carlo simulation methods. A robust Bayesian method is proposed to estimate,
Medical Analysis and Diagnosis by Neural Networks
 Medical Data Analysis, SpringerVerlag
, 2001
"... In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly wors ..."
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Cited by 8 (0 self)
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In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described.
Neural Knowledge Processing in Expert Systems
"... The knowledge base in expert systems usually contains different types of information which can be classified as explicit and implicit with respect to its representation. The explicit representation is based on a symbolic expression of human expert knowledge while the numerical data which require add ..."
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Cited by 7 (0 self)
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The knowledge base in expert systems usually contains different types of information which can be classified as explicit and implicit with respect to its representation. The explicit representation is based on a symbolic expression of human expert knowledge while the numerical data which require additional processing to be really understood represent the implicit knowledge. The rulebased systems and neural networks are typical examples of these different representation approaches. The main problem of rulebased systems is the knowledge acquisition which can be overcoming by learning and adaptation in neural networks. On the other hand, the neural implicit knowledge representation loses the capability to explain and justify the inference. Thus, the advantages and disadvantages of explicit and implicit knowledge representation in expert systems are complementary and we will first give a general comparison of both. Then we will discuss how to process the neural knowledge to embed it into...
A Mixture Model System for Medical and Machine Diagnosis
 Advances in Neural Information Processing System 7
, 1995
"... Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distributionof the data can be used to solve this problem. We model this with mixture models whose parameters are estim ..."
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Cited by 6 (4 self)
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Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distributionof the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented. 1 INTRODUCTION Diagnosis is the process of identifying diseases in patients or disorders in machines by considering history, symptoms and other signs through examination. Diagnosis is a common and important problem that has proven hard to automate and formalize. A procedural descr...