Approximation contexts in addressing graph data structures

While the application of machine learning algorithms to practical problems has been expanded from fixed sized input data to sequences, trees or graphs input data, the composition of learning system has developed from a single model to integrated ones. Recent advances in graph based learning algorithms include: the SOMSD (Self Organizing Map for Structured Data), PMGraphSOM (Probability Measure Graph Self Organizing Map,GNN (Graph Neural Network) and GLSVM (Graph Laplacian Support Vector Machine). A main motivation of this thesis is to investigate if such algorithms, whether by themselves individually or modified, or in various combinations, would provide better performance over the more traditional artificial neural networks or kernel machine methods on some practical challenging problems. More succinctly, this thesis seeks to answer the main research question: when or under what conditions/contexts could graph based models be adjusted and tailored to be most efficacious in terms of predictive or classification performance on some challenging practical problems? There emerges a range of sub-questions including: how do we craft an effective neural learning system which can be an integration of several graph and non-graph based models? Integration of various graph based and non graph based kernel machine algorithms; enhancing the capability of the integrated model in working with challenging problems; tackling the problem of long term dependency issues which aggravate the performance of layer-wise graph based neural systems. This thesis will answer these questions. Recent research on multiple staged learning models has demonstrated the efficacy of multiple layers of alternating unsupervised and supervised learning approaches. This underlies the very successful front-end feature extraction techniques in deep neural networks. However much exploration is still possible with the investigation of the number of layers required, and the types of unsupervised or supervised learning models which should be used. Such issues have not been considered so far, when the underlying input data structure is in the form of a graph. We will explore empirically the capabilities of models of increasing complexities, the combination of the unsupervised learning algorithms, SOM, or PMGraphSOM, with or without a cascade connection with a multilayer perceptron, and with or without being followed by multiple layers of GNN. Such studies explore the effects of including or ignoring context. A parallel study involving kernel machines with or without graph inputs has also been conducted empirically

Agrupamiento conceptual jerárquico basado en distancias: Definición e instanciación para el caso proposicional

Se presenta una aproximación que integra el agrupamiento jerárquico basado en distancias y el conceptual. Muchas inconsistencias pueden surgir entre la distancia y las generalizaciones, por lo que proponemos un marco conceptual para el análisis de inconsistencias. También se presenta una instanciación del marco para agrupamiento proposicional.Funes, AM. (2008). Agrupamiento conceptual jerárquico basado en distancias: Definición e instanciación para el caso proposicional. http://hdl.handle.net/10251/13621Archivo delegad

Hybrid intelligent approaches for business process sequential analysis.

The quality of customer services is an important differentiator for service oriented com- panies like telecommunication providers. In order to deliver good customer service, the underlying processes within the operations of a company have to run smoothly and must be well controlled. It is of great importance to be able to predict if processes are likely to fail and to be aware of developing problems as early as possible. A failure in a customer service process typically results in a negative experience for a customer and companies are keen to avoid this from happening. Process performance prediction allows companies to pro-actively adapt with process execution in order to prevent process problems from affect- ing their customers. Process analytics is often compounded by a number of factors. Very often processes are only poorly documented because they have evolved over time together with the legacy IT systems that were used to implement them. The workflow data that is collected during process execution is high dimensional and can contain complex attributes and very diverse values. Since workflow data is sequential in nature, there are a number of data mining methods such as sequential pattern mining and probabilistic models that can be useful for predicting process transitions or process outcomes. None of these techniques alone can adequately cope with workflow data. The purpose of this thesis is to contribute a combination of methods that can analyse data from business process in execution in order to predict severe process incidents. In order to best exploit the sequential nature of the data we have used a number of sequential data mining approaches coupled with sequence alignment and a strategy for dealing with similar sequences. The methods have been applied to real process data from a large telecommunication provider and we have conducted a number of experiments demonstrating how to predict process steps and process outcomes. Finally, we show that the performance of the proposed models can be significantly improved if they are applied to individual clusters of workflow data rather than the complete set of process data

Reservoir Computing for Learning in Structured Domains

The study of learning models for direct processing complex data structures has gained an increasing interest within the Machine Learning (ML) community during the last decades. In this concern, efficiency, effectiveness and adaptivity of the ML models on large classes of data structures represent challenging and open research issues. The paradigm under consideration is Reservoir Computing (RC), a novel and extremely efficient methodology for modeling Recurrent Neural Networks (RNN) for adaptive sequence processing. RC comprises a number of different neural models, among which the Echo State Network (ESN) probably represents the most popular, used and studied one. Another research area of interest is represented by Recursive Neural Networks (RecNNs), constituting a class of neural network models recently proposed for dealing with hierarchical data structures directly. In this thesis the RC paradigm is investigated and suitably generalized in order to approach the problems arising from learning in structured domains. The research studies described in this thesis cover classes of data structures characterized by increasing complexity, from sequences, to trees and graphs structures. Accordingly, the research focus goes progressively from the analysis of standard ESNs for sequence processing, to the development of new models for trees and graphs structured domains. The analysis of ESNs for sequence processing addresses the interesting problem of identifying and characterizing the relevant factors which influence the reservoir dynamics and the ESN performance. Promising applications of ESNs in the emerging field of Ambient Assisted Living are also presented and discussed. Moving towards highly structured data representations, the ESN model is extended to deal with complex structures directly, resulting in the proposed TreeESN, which is suitable for domains comprising hierarchical structures, and Graph-ESN, which generalizes the approach to a large class of cyclic/acyclic directed/undirected labeled graphs. TreeESNs and GraphESNs represent both novel RC models for structured data and extremely efficient approaches for modeling RecNNs, eventually contributing to the definition of an RC framework for learning in structured domains. The problem of adaptively exploiting the state space in GraphESNs is also investigated, with specific regard to tasks in which input graphs are required to be mapped into flat vectorial outputs, resulting in the GraphESN-wnn and GraphESN-NG models. As a further point, the generalization performance of the proposed models is evaluated considering both artificial and complex real-world tasks from different application domains, including Chemistry, Toxicology and Document Processing

Using Logical Decision Trees for Clustering

A novel first order clustering system, called C 0.5, is presented. It inherits its logical decision tree formalism from the TILDE system, but instead of using class information to guide the search, it employs the principles of instance based learning in order to perform clustering. Various experiments are discussed, which show the promise of the approach. 1 Introduction A decision tree is usually seen as representing a theory for classification of examples. If the examples are positive and negative examples for one specific concept, then the tree defines these two concepts. One could also say, if there are k classes, that the tree defines k concepts. Another viewpoint is taken in Langley's Elements of Machine Learning [Langley, 1996]. Langley sees decision tree induction as a special case of the induction of concept hierarchies. A concept is associated with each node of the tree, and as such the tree represents a kind of taxonomy, a hierarchy of many concepts. This is very similar to ..

Using Logical Decision Trees for Clustering

A novel first order clustering system, called C 0.5, is presented. It inherits its logical decision tree formalism from the TILDE system, but instead of using class information to guide the search, it employs the principles of instance based learning in order to perform clustering. Various experiments are discussed, which show the promise of the approach. 1 Introduction A decision tree is usually seen as representing a theory for classification of examples. If the examples are positive and negative examples for one specific concept, then the tree defines these two concepts. One could also say, if there are k classes, that the tree defines k concepts. Another viewpoint is taken in Langley's Elements of Machine Learning [ Langley, 1996 ] . Langley sees decision tree induction as a special case of the induction of concept hierarchies. A concept is associated with each node of the tree, and as such the tree represents a kind of taxonomy, a hierarchy of many concepts. This is very similar..