Comparative performances of stochastic competitive evolutionary neural tree (SCENT) with neural classifiers

A stochastic competitive evolutionary neural tree (SCENT) is described and evaluated against the best neural classifiers with equivalent functionality, using a collection of data sets chosen to provide a variety of clustering scenarios. SCENT is firstly shown to produce flat classifications at least as well as the other two neural classifiers used. Moreover its variability in performance over the data sets is shown to be small. In addition SCENT also produces a tree that can show any hierarchical structure contained in the data. For two real world data sets the tree captures hierarchical features of the data.Peer reviewe

Hierarchical topological clustering learns stock market sectors

The breakdown of financial markets into sectors provides an intuitive classification for groups of companies. The allocation of a company to a sector is an expert task, in which the company is classified by the activity that most closely describes the nature of the company's business. Individual share price movement is dependent upon many factors, but there is an expectation for shares within a market sector to move broadly together. We are interested in discovering if share closing prices do move together, and whether groups of shares that do move together are identifiable in terms of industrial activity. Using TreeGNG, a hierarchical clustering algorithm, on a time series of share closing prices, we have identified groups of companies that cluster into clearly identifiable groups. These clusters compare favourably to a globally accepted sector classification scheme, and in our opinion, our method identifies sector structure clearer than a statistical agglomerative hierarchical clustering metho

Optimising a stochastic dynamic neural tree

Peer reviewe

Optimising a neural tree classifier using a genetic algorithm

This paper documents experiments performed using a GA to optimise the parameters of a dynamic neural tree model. Two fitness functions were created from two selected clustering measures, and a population of genotypes, specifying parameters of the model were evolved. This process mirrors genomic evolution and ontogeny. It is shown that the evolved parameter values improved performanceFinal Accepted Versio

An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/ISCIT.2004.1413871Rainfall prediction is a challenging task especially in a modern world facing the major environmental problem of global warming. The proposed method uses an Adaptive Radial Basis Function neural network mode with a specially designed gerietic algoruhm (CA) to obtain the optimal model parameters. A significant feature of the Adaptive Radinl Basis Function network is that it is able creak new hidden units and solve the spread factor problem using a genetic algorithm. It is shown that the evolved parameter values improved performance

Optimising a hierarchical neural clusterer applied to large gene sequence data sets

Original article can be found at: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=9314Evolutionary Algorithms have been used to optimise the performance of neural network models before. This paper uses a hybrid approach by permanently attaching a Genetic Algorithm (GA) to a hierarchical clusterer to investigate appropriate parameter values for producing specific tree shaped representations for some gene sequence data. It addresses a particular problem where the size of the data set makes the direct use of a GA too time consuming. We show by using a data set nearly two orders of magnitude smaller in the GA investigation that the results can be usefully translated across to the real, much larger data sets. The data sets in question are gene sequences and the aim of the analysis was to cluster short sub-sequences that could represent binding sites that regulate the expression of genes

Stochastic dynamic hierarchical neural networks

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