Browsing by Author "van Zyl, Jean-Pierre"

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    Rule Induction with Swarm Intelligence
    (Stellenbosch : Stellenbosch University, 2022-03) van Zyl, Jean-Pierre; Engelbrecht, Andries Petrus; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: Rule induction is the process by which explainable mappings are created between a set of input data instances and a set of labels for the input instances. This process can be seen as an extension of traditional classification algorithms, because rule induction algorithms perform classification b ut h ave t he addedproperty of being transparent when making inferences. Popular algorithms in existing literature tend to use antiquated approaches to induce rule sets. The existing approaches tend to be greedy in nature and do not provide a platform for algorithm expansion or improvement. This thesis investigates a new approach to rule induction using a set-based particle swarm optimisation algorithm. The investigation starts with a comprehensive review of the relevant literature, after which the novel algorithm is proposed and compared with popular rule induction algorithms. After the establishment of the capabilities and validity of the set-based particle swarm optimisation rule induction algorithm, the effect of the objective function on the algorithm is investigated. The objective function is tested with 12 existing performance evaluation metrics in order to understand how the performance of the algorithm can be improved. These 12 existing metrics are then used as inspiration for the proposal of 11 new performance evaluation metrics which are also tested as part of the objective function effect analysis. The effect o f v arying d istributions o f t he v alues o f t he t arget c lass i s also examined. This thesis also investigates the reformulation of the rule induction problem as a multi-objective optimisation problem and applies the newly developed multi-guide set-based particle swarm optimisation algorithm to the multiobjective formulation of rule induction. The performance of rule induction as a multi-objective problem is evaluated by examining how the trade-off between the defined objectives functions affects performance for different datasets. The existing metrics and newly proposed metrics tested in the single objective formulation of the rule induction problem are also tested in the multi-objective formulation.