Engineering and Information Technology Collected Works - Research Publications

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    Engineering Blockchain Based Software Systems: Foundations, Survey, and Future Directions
    Fahmideh, M ; Grundy, J ; Ahmed, A ; Shen, J ; Yan, J ; Mougouei, D ; Wang, P ; Ghose, A ; Gunawardana, A ; Aickelin, U ; Abedin, B ( 2021-05-05)
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    Multi-objective Semi-supervised Clustering for Finding Predictive Clusters
    Ghasemi, Z ; Khorshidi, HA ; Aickelin, U ( 2022-01-26)
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    Stability Classification of Stock Market Using Temporal Rule-Based Classification
    Fattah, P ; Aickelin, U ; Wagner, C ; Rashid, TA ; Alsadoon, A ; Bacanin, N ( 2022-10-10)
    Abstract Economists have sought to predict stock market prices for decades with varying degrees of success. This study classifies stocks according to their stability in two consequent financial quarters (depending on whether the majority of stocks remain in the same stability group, which can indicate forecastability). However, classifying temporal information like stock market data using available methods produces complicated rules that cannot be easily interpreted by human experts. To reduce this complication a new approach of rule-based temporal classification is used. The method combines human-provided rules with machine optimisation to produce classes that can be easily interpreted by experts, enabling them to comprehend the complicated temporal dimension of the data. Rules provided by human experts might be via generalization of the temporal data using statistical functions like standard deviation and averages. Initially, each rule will have a range of possible values that can be reduced to a single one. For this study, we classify the stability of stock market data of the S&P 500 for two constitutive financial quarters to test if stocks have the same stability or not. The results show that the classes for stock markets are different beyond random chance, which might be an indication of the viability of forecasting stock markets depending on their old trends.
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    A Synthetic Over-sampling method with Minority and Majority classes for imbalance problems
    Khorshidi, HA ; Aickelin, U ( 2020-11-08)
    Class imbalance is a substantial challenge in classifying many real-world cases. Synthetic over-sampling methods have been effective to improve the performance of classifiers for imbalance problems. However, most synthetic oversampling methods generate non-diverse synthetic instances within the convex hull formed by the existing minority instances as they only concentrate on the minority class and ignore the vast information provided by the majority class. They also often do not perform well for extremely imbalanced data as the fewer the minority instances, the less information to generate synthetic instances. Moreover, existing methods that generate synthetic instances using the majority class distributional information cannot perform effectively when the majority class has a multimodal distribution. We propose a new method to generate diverse and adaptable synthetic instances using Synthetic Over-sampling with Minority and Majority classes (SOMM). SOMM generates synthetic instances diversely within the minority data space. It updates the generated instances adaptively to the neighbourhood including both classes. Thus, SOMM performs well for both binary and multiclass imbalance problems. We examine the performance of SOMM for binary and multiclass problems using benchmark data sets for different imbalance levels. The empirical results show the superiority of SOMM compared to other existing methods.