Handling Concept Drift in the Context of Expensive Labels

Machine learning has been successfully applied to a wide range of prediction problems, yet its application to data streams can be complicated by concept drift. Existing approaches to handling concept drift are overwhelmingly reliant on the assumption that it is possible to obtain the true label of an instance shortly after classification at a negligible cost. The aim of this thesis is to examine, and attempt to address, some of the problems related to handling concept drift when the cost of obtaining labels is high. This thesis presents Decision Value Sampling (DVS), a novel concept drift handling approach which periodically chooses a small number of the most useful instances to label. The newly labelled instances are then used to re-train the classifier, an SVM with a linear kernel, to handle any change in concept that might occur. In this way, only the instances that are required to keep the classifier up-to-date are labelled. The evaluation of the system indicates that a classifier can be kept up-to-date with changes in concept while only requiring 15% of the data stream to be labelled. In a data stream with a high throughput this represents a significant reduction in the number of labels required. The second novel concept drift handling approach proposed in this thesis is Confidence Distribution Batch Detection (CDBD). CDBD uses a heuristic based on the distribution of an SVM’s confidence in its predictions to decide when to rebuild the clas- sifier. The evaluation shows that CDBD can be used to reliably detect when a change in concept has taken place and that concept drift can be handled if the classifier is rebuilt when CDBD sig- nals a change in concept. The evaluation also shows that CDBD obtains a considerable labels saving as it only requires labelled data when a change in concept has been detected. The two concept drift handling approaches deal with concept drift in a different manner, DVS continuously adapts the clas- sifier, whereas CDBD only adapts the classifier when a sizeable change in concept is suspected. They reflect a divide also found in the literature, between continuous rebuild approaches (like DVS) and triggered rebuild approaches (like CDBD). The final major contribution in this thesis is a comparison between continuous and triggered rebuild approaches, as this is an underexplored area. An empirical comparison between representative techniques from both types of approaches shows that triggered rebuild works slightly better on large datasets where the changes in concepts occur infrequently, but in general a continuous rebuild approach works the best

Anomaly-based network intrusion detection enhancement by prediction threshold adaptation of binary classification models

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model’s accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold."This research was supported and funded by the Government of the Sultanate of Oman represented by the Ministry of Higher Education and the Sultan Qaboos University." -- p. i

Quantitative Comparison of Benthic Habitat Maps Derived From Multibeam Echosounder Backscatter Data

In the last decade, following the growing concern for the conservation of marine ecosystems, a wide range of approaches has been developed to achieve the identification, classification and mapping of seabed types and of benthic habitats. These approaches, commonly grouped under the denominations of Benthic Habitat Mapping or Acoustic Seabed Classification, exploit the latest scientific and engineering advancements for the exploration of the bottom of the ocean, particularly in underwater acoustics. Among all acoustic seabed-mapping systems available for this purpose, a growing interest has recently developed for Multibeam Echosounders (MBES). This interest is mainly the result of the multiplicity of these systems’ outputs (that is, bathymetry, backscatter mosaic, angular response and water-column data), which allows for multiple approaches to seabed or habitat classification and mapping. While this diversity of mapping approaches and this multiplicity of MBES data products contribute to an increasing quality of the charting of the marine environment, they also unfortunately delay the future standardization of mapping methods, which is required for their effective integration in marine environment management strategies. As a preliminary step towards such standardization, there is a need for generalized efforts of comparison of systems, data products, and mapping approaches, in order to assess the most effective ones given mapping objectives and environment conditions. The main goal of this thesis is to contribute to this effort through the development and implementation of tools and methods for the comparison of categorical seabed or habitat maps, with a specific focus on maps obtained from up-to-date methodologies of classification of MBES backscatter data. This goal is attained through the achievement of specific objectives treated sequentially. First, the need for comparison is justified through a review of the diversity characterizing the fields of Benthic Habitat Mapping and Acoustic Seabed Classification, and of their use of MBES data products. Then, a case study is presented that compare the data products from a Kongsberg EM3000 MBES to the output map of an Acoustic Ground Discrimination Software based on data from a Single-beam Echosounder and to a Sidescan Sonar mosaic, in order to illustrate how map comparison measures could contribute to the comparison of these systems. Next, a number of measures for map-to-map comparison, inspired from the literature in land remote sensing, are presented, along with methodologies for their implementation in comparison of maps described with different legends. The benefit of these measures and methodologies is demonstrated through their application to maps obtained from the acoustic datasets presented previously. Finally, a more typical implementation of these measures is presented as a case study in which the development of two up-to-date classification methodologies of MBES backscatter data is complemented by the quantitative comparison of their output maps. In the process of developing and illustrating the use of methods for the assessment of map-to-map similarity, this thesis also presents methodologies for the processing and classification of backscatter data from MBES. In particular, the potential of the combined use of the spatial and angular information of these data for seabed classification is explored through the development of an original segmentation methodology that sequentially divides and aggregates segments defined from a MBES backscatter mosaic on the basis of their angular response content

Online semi-supervised learning in non-stationary environments

Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and balanced data, immediately or after some delay, to extract worthwhile knowledge from the continuous and rapid data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet. Manual labelling of these data streams is not practical due to time consumption and the need for domain expertise. Another challenge is learning under Non-Stationary Environments (NSEs), which occurs due to changes in the data distributions in a set of input variables and/or class labels. The problem of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms have no access to the true class labels directly when the concept evolves. Several approaches exist that deal with NSE and EVL in isolation. However, few algorithms address both issues simultaneously. This research directly responds to ILNSE’s challenge in proposing two novel algorithms “Predictor for Streaming Data with Scarce Labels” (PSDSL) and Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label scarcity issues in online machine learning. The key capabilities of PSDSL include learning from a small amount of labelled data in an incremental or online manner and being available to predict at any time. To achieve this, PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it continuously learns from incoming data and updates the model as new labelled or unlabelled data becomes available over time. Furthermore, it can predict under NSE conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier, which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch and adapt to the conditions. The PSDSL adapts to learning states between self-learning, micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of the data stream. HDWM makes use of “seed” learners of different types in an ensemble to maintain its diversity. The ensembles are simply the combination of predictive models grouped to improve the predictive performance of a single classifier. PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than existing approaches on most real-time data streams including randomised data instances. PSDSL performed significantly better than ‘Static’ i.e. the classifier is not updated after it is trained with the first examples in the data streams. When applied to MOA-generated data streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC, while SCARGC performed the same as the Static. PSDSL achieved better average prediction accuracies in a short time than SCARGC. The HDWM algorithm is evaluated on artificial and real-world data streams against existing well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic DWM algorithm. The results showed that HDWM performed significantly better than WMA and DWM. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM. In both drift and real-world streams, significance tests and post hoc comparisons found significant differences between algorithms, HDWM performed significantly better than DWM and WMA when applied to MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms benefit from the use of both forgetting and retaining the models. The algorithm also provides the independence of selecting the optimal base classifier in its ensemble depending on the problem. A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts during the cluster labelling process. In this process, PSDSL transforms the centroids’ information of micro-clusters into micro-instances and generates new clusters called Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and successfully guide the cluster labelling process after the concept drifts in the absence of true class labels. PSDSL has been evaluated on real-world problem ‘keystroke dynamics’, and the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC (81.6%), while the Static (49.0%) significantly degrades the performance due to changes in the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found highly fluctuated between (41.1% to 81.6%) based on different values of parameter ‘k’ (number of clusters), while PSDSL automatically determine the best values for this parameter

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

Applications of Molecular Dynamics simulations for biomolecular systems and improvements to density-based clustering in the analysis

Molecular Dynamics simulations provide a powerful tool to study biomolecular systems with atomistic detail. The key to better understand the function and behaviour of these molecules can often be found in their structural variability. Simulations can help to expose this information that is otherwise experimentally hard or impossible to attain. This work covers two application examples for which a sampling and a characterisation of the conformational ensemble could reveal the structural basis to answer a topical research question. For the fungal toxin phalloidin—a small bicyclic peptide—observed product ratios in different cyclisation reactions could be rationalised by assessing the conformational pre-organisation of precursor fragments. For the C-type lectin receptor langerin, conformational changes induced by different side-chain protonations could deliver an explanation of the pH-dependency in the protein’s calcium-binding. The investigations were accompanied by the continued development of a density-based clustering protocol into a respective software package, which is generally well applicable for the use case of extracting conformational states from Molecular Dynamics data