Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions

This research focuses mainly on the binary class imbalance problem in data mining. It investigates the use of combined approaches of data and algorithmic level solutions. Moreover, it examines the use of swarm intelligence and population-based techniques to combat the class imbalance problem at all levels, including at the data, algorithmic, and feature level. It also introduces various solutions to the class imbalance problem, in which swarm intelligence techniques like Stochastic Diffusion Search (SDS) and Dispersive Flies Optimisation (DFO) are used. The algorithms were evaluated using experiments on imbalanced datasets, in which the Support Vector Machine (SVM) was used as a classifier. SDS was used to perform informed undersampling of the majority class to balance the dataset. The results indicate that this algorithm improves the classifier performance and can be used on imbalanced datasets. Moreover, SDS was extended further to perform feature selection on high dimensional datasets. Experimental results show that SDS can be used to perform feature selection and improve the classifier performance on imbalanced datasets. Further experiments evaluated DFO as an algorithmic level solution to optimise the SVM kernel parameters when learning from imbalanced datasets. Based on the promising results of DFO in these experiments, the novel approach was extended further to provide a hybrid algorithm that simultaneously optimises the kernel parameters and performs feature selection

Solution Biases and Pheromone Representation Selection in Ant Colony Optimisation.

Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distribution, timetabling, resource allocation and project management all feature problems where the solution is some combination of elements, the overall value of which needs to be either maximised or minimised (i.e., optimised), typically subject to a number of constraints. Thus, techniques to efficiently solve such problems are an important area of research. A popular group of optimisation algorithms are the metaheuristics, approaches that specify how to search the space of solutions in a problem independent way so that high quality solutions are likely to result in a reasonable amount of computational time. Although metaheuristic algorithms are specified in a problem independent manner, they must be tailored to suit each particular problem to which they are applied. This thesis investigates a number of aspects of the application of the relatively new Ant Colony Optimisation (ACO) metaheuristic to different COPs. The standard ACO metaheuristic is a constructive algorithm loosely based on the foraging behaviour of ant colonies, which are able to find the shortest path to a food source by indirect communication through pheromones. ACO’s artificial pheromone represents a model of the solution components that its artificial ants use to construct solutions. Developing an appropriate pheromone representation is a key aspect of the application of ACO to a problem. An examination of existing ACO applications and the constructive approach more generally reveals how the metaheuristic can be applied more systematically across a range of COPs. The two main issues addressed in this thesis are biases inherent in the constructive process and the systematic selection of pheromone representations. The systematisation of ACO should lead to more consistently high performance of the algorithm across different problems. Additionally, it supports the creation of a generalised ACO system, capable of adapting itself to suit many different combinatorial problems without the need for manual intervention

Intrusion detection by machine learning = Behatolás detektálás gépi tanulás által

Since the early days of information technology, there have been many stakeholders who used the technological capabilities for their own benefit, be it legal operations, or illegal access to computational assets and sensitive information. Every year, businesses invest large amounts of effort into upgrading their IT infrastructure, yet, even today, they are unprepared to protect their most valuable assets: data and knowledge. This lack of protection was the main reason for the creation of this dissertation. During this study, intrusion detection, a field of information security, is evaluated through the use of several machine learning models performing signature and hybrid detection. This is a challenging field, mainly due to the high velocity and imbalanced nature of network traffic. To construct machine learning models capable of intrusion detection, the applied methodologies were the CRISP-DM process model designed to help data scientists with the planning, creation and integration of machine learning models into a business information infrastructure, and design science research interested in answering research questions with information technology artefacts. The two methodologies have a lot in common, which is further elaborated in the study. The goals of this dissertation were two-fold: first, to create an intrusion detector that could provide a high level of intrusion detection performance measured using accuracy and recall and second, to identify potential techniques that can increase intrusion detection performance. Out of the designed models, a hybrid autoencoder + stacking neural network model managed to achieve detection performance comparable to the best models that appeared in the related literature, with good detections on minority classes. To achieve this result, the techniques identified were synthetic sampling, advanced hyperparameter optimization, model ensembles and autoencoder networks. In addition, the dissertation set up a soft hierarchy among the different detection techniques in terms of performance and provides a brief outlook on potential future practical applications of network intrusion detection models as well

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda

Die vorliegende Dissertation mit dem Titel “Towards understanding of climbing, tip-over prevention and self-righting behaviors in Hexapoda” untersucht in drei Studien exemplarisch, wie (i) Wüstenameisen ihre Beine einsetzen um An- und Abstiege zu überwinden, wie (ii) Wüsten- und Waldameisen ein Umkippen an steilen Anstiegen vermeiden, und wie sich (iii) Madagaskar-Fauchschaben, Amerikanische Großschaben und Blaberus discoidalis Audinet-Servill, 1839 aus Rückenlagen drehen und aufrichten. Neuartige biomechanischen Beschreibungen umfassen unter anderem: Impuls- und Kraftwirkungen einzelner Ameisenbeine auf den Untergrund beim Bergauf- und Bergabklettern, Kippmomente bei kletternden Ameisen, Energiegebirge-Modelle (energy landscapes) zur Quantifizierung der Körperform für die funktionelle Beschreibung des Umdrehens aus der Rückenlage

Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem