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In this thesis we address the problem of learning various types of decision trees from timechanging data streams. In particular, we study online machine learning algorithms for learning regression trees, linear model trees, option trees for regression, multi-target model trees, and ensembles of model trees from data streams. These are …

The domain of data mining (DM) deals with analyzing different types of data. The data typically used in data mining is in the format of a single table, with primitive datatypes as attributes. However, structured (complex) data, such as graphs, sequences, networks, text, image, multimedia and relational data, are receiving …

Feature ranking is the machine learning task of inducing an ordering of features in a given dataset according to some notion of relevance. We consider the feature ranking task in the context of supervised learning, where the notion of feature relevance is defined with respect to a target concept. Feature …

Can a model constructed by machine learning or data mining programs be trusted? For example, it is known that a decision tree model can contain less-credible parts caused by pathologies in induction algorithms, noise and missing values in data, or simply because of the complexity of a domain. Such models …

This thesis addresses the task of formalizing and implementing the process of semi-automatic ontology construction. We propose a theoretical framework for formalizing the ontology construction process. The process is described as a sequence of operators applied to the ontology. Several types of common operators are identified and each type is …

Solving real-life optimization problems numerically is often very time demanding, because of high complexity of the simulations that are usually involved. Solving such problems becomes highly impractical for this reason and can even lead to use of less complex and also less accurate models. Fortunately, evolutionary algorithms, often used in …

Over the last decade we have been witnessing rapid development of telecommunication networks and services, in particular in the filed of new wireless network technologies. On different markets users are able to communicate using different wireless technologies. The first two generation of mobile networks (i.e. NMT and GSM) were developed …

The goal of knowledge discovery in databases is to construct models or discover interesting patterns in data. Model construction and pattern discovery are frequently performed by rule learning, as the induced rules are easy to be interpreted by human experts. The standard classification rule learning task is to induce classification/prediction …

In language technologies, syntactic parsing represents one of the possible intermediate steps of text analysis in the applications such as machine translation, information extraction, question answering, etc. Syntactic trees are often used to demonstrate the structure of text. In the last decades, the dependency framework became a popular syntactic representation, …

Developing metaheuristics to solve optimization problems is a rapidly growing field of research. This is due to the importance of optimization problems in the scientific as well as the industrial world. The methods developed in this dissertation are based on stigmergy: a method of communication in emergent systems, where the …