Investigating Randomised Sphere Covers in Supervised Learning

c©This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no quotation from the thesis, nor any information derived therefrom, may be published without the author’s prior, written consent. In this thesis, we thoroughly investigate a simple Instance Based Learning (IBL) classifier known as Sphere Cover. We propose a simple Randomized Sphere Cover Classifier (αRSC) and use several datasets in order to evaluate the classification performance of the αRSC classifier. In addition, we analyse the generalization error of the proposed classifier using bias/variance decomposition. A Sphere Cover Classifier may be described from the compression scheme which stipulates data compression as the reason for high generalization performance. We investigate the compression capacity of αRSC using a sample compression bound. The Compression Scheme prompted us to search new compressibility methods for αRSC. As such, we used a Gaussian kernel to investigate further data compression

Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Advancements in Multi-Layer Perceptron Training to Improve Classification Accuracy

Neural Networks are the popular classification tools used in Medical diagnosis for early disease detection. The performance of Neural Networks is highly depended on the training process. In the training process, the individual weights between each of the neuron are adjusted for better classification results. Many Gradient-based and Meta-heuristic training algorithms are proposed and used by the researchers to improve the training performance of Neural Network. However, there are some limitations in both Gradient-based and Meta-heuristic algorithms when there are used individually. To overcome these limitations and to improve the Multi-Layer Perceptron Network performance Hybrid algorithms are useful. In this study, a review on advancements in Multi-Layer Perceptron Network training process for the improvement of classification performance is presented

Combining heterogeneous classifiers via granular prototypes.

In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e., Decision Template and Two Stages Ensemble System, AdaBoost, Random Forest, L2-loss Linear Support Vector Machine, and Decision Tree

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research