Automatic sorting of point pattern sets using Minkowski Functionals

Point pattern sets arise in many different areas of physical, biological, and applied research, representing many random realizations of underlying pattern formation mechanisms. These pattern sets can be heterogeneous with respect to underlying spatial processes, which may not be visually distinguishable. This heterogeneity can be elucidated by looking at statistical measures of the patterns sets and using these measures to divide the pattern set into distinct groups representing like spatial processes. We introduce here a numerical procedure for sorting point pattern sets into spatially homogeneous groups using Functional Principal Component Analysis (FPCA) applied to the approximated Minkowski functionals of each pattern. We demonstrate that this procedure correctly sorts pattern sets into similar groups both when the patterns are drawn from similar processes and when the 2nd-order characteristics of the pattern are identical. We highlight this routine for distinguishing the molecular patterning of fluorescently labeled cell membrane proteins, a subject of much interest in studies investigating complex spatial signaling patterns involved in the human immune response.Comment: 11 pages, 6 figures, submitted to Physical Review E (05 March 2013

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST