For high dimensional data, if no preprocessing is carried out before inputting patterns to classifiers, the computation required may be too heavy. For example, the number of hidden units of a radial basis function (RBF) neural network can be too large. This is not suitable for some practical applications due to speed and memory constraints. In many cases, some attributes are not relevant to concepts in the data at all. In this paper, we propose a novel separability-correlation measure (SCM) to rank the importance of attributes. According to the attribute ranking results, different attribute subsets are used as inputs to a classifier, such as an RBF neural network. Those attributes that increase the validation error are deemed irrelevant and are deleted. The complexity of the classifier can thus be reduced and its classification performance improved. Computer simulations show that our method for attribute importance ranking leads to smaller attribute subsets with higher accuracies compared with the existing SUD and Relief-F methods. We also propose a modified method for efficient construction of an RBF classifier. In this method we allow for large overlaps between clusters corresponding to the same class label. Our approach significantly reduces the structural complexity of the RBF network and improves the classification performance.

/pdf/data-dimensionality-reduction-with-application-to-3jipl9stn9.pdf

Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance

Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems

A four layer fuzzy neural network is presented to realise knowledge acquisition from input-output samples. The network parameters including the necessary membership functions of the input variables and the consequent parameters are tuned and identified using a modified particle swarm algorithm which uses each particle's best current performance of its neighbours to replace the best previous one and uses a non accumulative rate of change to replace the accumulative one for accelerating search procedure. The trained network is then pruned so that the general rules can be extracted and explained. The experimental results have shown that the similar classification rules can be obtained in comparison to that of other fuzzy neural approaches.

Extracting rules from fuzzy neural network by particle swarm optimisation

An alternative approach for generation of membership functions and fuzzy rules based on radial and cubic basis function networks

This paper presents a new algorithm for the construction and training of an RBF neural network with unbalanced data. In applications, minority classes with much fewer samples are often present in data sets. The learning process of a neural network usually is biased towards classes with majority populations. Our study focused on improving the classification accuracy of minority classes while maintaining the overall classification performance.

/pdf/training-rbf-neural-networks-on-unbalanced-data-29ydbwsucv.pdf

Training RBF neural networks on unbalanced data

We summarize our results from investigating different learning and classification algorithms for basis function limited neural networks To achieve fast convergence we used RCE type learning procedures that have been modified for our applications and to enable simple hardware implementability The used radial and cubic basis functions are a signum type function, a ramp function and a gaussian function We investigated the learning algorithms to find fast and efficient procedures to automatically extract fuzzy rules and membership functions from high dimensional data which is topic of another paper >

RBF and CBF neural network learning procedures

A new method is presented combining the advantages of fuzzy inference and neural network learning. A three-layer radial basis function (RBF) network is used to extract rules and to identify the necessary membership functions of the inputs for a fuzzy classification system. The results obtained applying this new method to IRIS-classification are similar to that of other fuzzy-neural approaches, but only lesser number of rules and membership functions are necessary. This system based on RBF-neurons and extended restricted coulomb energy (RCE) learning allows very fast construction of expert knowledge only from input/output data without externally provided expert help and superfluous input features can be removed automatically after training the network. >

A new method for generating fuzzy classification systems using rbf neurons with extended rce learning

A new method for generating fuzzy classification systems using RBF neurons with extended RCE learning

We summarize our results from investigating different learning and classification algorithms for basis function limited neural networks. To achieve fast convergence we used RCE type learning procedures that have been modified for our applications and to enable simple hardware implementability. The used radial and cubic basis functions are a signum type function, a ramp function and a gaussian function. We investigated the learning algorithms to find fast and efficient procedures to automatically extract fuzzy rules and membership functions from high dimensional data which is topic of another paper.<<ETX>>

Rbf and cbf neural network learning procedures

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RBF and CBF neural network learning procedures

A new method for generating fuzzy classification systems using rbf neurons with extended rce learning

A new method for generating fuzzy classification systems using RBF neurons with extended RCE learning

Rbf and cbf neural network learning procedures