Combining Feature Space Refinement and Outcome Accumulation Type Evolutionary Rule Discovery

What is it about?

An outcome accumulation-type evolutionary rule discovery method (GNMiner) has been proposed, which has a directed graph structure and is characterized by the accumulation of outcomes acquired in the evolutionary process. Because GNMiner is less efficient at discovery with respect to large feature sets, a feature-selection mechanism is required. In this paper, we propose a framework that integrates evolutionary rule discovery using GNMiner and evolutionary feature selection to refine the search space. The framework is characterized by problem-solving through an evolutionary process in which the two components are interdependent. The experimental results show that the proposed method increases the number of important rules discovered and enhances the fitness value of GNMiner individuals compared with conventional GNMiner. For feature selection, the results also show that evolutionary computation based on the acquired outcomes of GNMiner is effective.

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Photo by Logan Voss on Unsplash

Photo by Logan Voss on Unsplash

Why is it important?

The significance of this study lies in the fact that, in addition to the conventional evolution of rule discovery, it introduces an evolution of the feature space, thereby constituting a single, integrated evolutionary process.