Hierarchical Classification Using Evolutionary Strategy

Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Hierarchical Classification using Evolutionary Strategy (HC-ES). It was tested in eight datasets the G-Protein-Coupled Receptor (GPCR) and Enzyme Commission Codes (EC). The results are compared with other hierarchical classifier using the distance and hF-Measure

Coherent Hierarchical Multi-Label Classification Networks

Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label classification problem, exploits the hierarchy information in order to produce predictions coherent with the constraint and improve performance. We conduct an extensive experimental analysis showing the superior performance of C-HMCNN(h) when compared to state-of-the-art models.Comment: Neural Information Processing Systems 202

Hierarchical ensemble methods for protein function prediction

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware \u201cflat\u201d prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a \u201cconsensus\u201d ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research

Advancing the analysis of architectural fabric structures, neural networks and uncertainty

PhD ThesisIn current practice a plane stress framework comprising elastic moduli and Poisson’s ratios is most commonly used to represent the mechanical properties of architectural fabrics. This is often done to enable structural analysis utilising commercially available, non-specialist, finite element packages. Plane stress material models endeavour to fit a flat plane to the highly non-linear stress strain response surface of architectural fabric. Neural networks have been identified as a possible alternative to plane stress material models. Through a process of training they are capable of capturing the relationship between experimental input and output data. With the addition of historical inputs and internal variables it has been demonstrated that neural network models are capable of representing complex history dependant behaviour. The resulting neural network architectural fabric material models have been implemented within custom large strain finite element code. The finite element code, capable of using either a neural network or plane stress material model, utilises a dynamic relaxation solution algorithm and includes geodesic string control for soap film form-finding. Analytical FORM reliability analysis using implied stiffness matrices' derived from the equations of the neural network model has also been investigated