Syahaneim Marzukhi
ABSTRACT
In existing artificial classification systems, the problem domain is created and controlled by humans. Humans set up and tune the problem domain, such as determining the problem's complexity. If humans can set up the problem appropriately then the machines can extract beneficial knowledge to solve classification task. This paper introduces an autonomous classification problem generation approach. The classification problem's difficulty is adapted based on the classification agent's performance within the defined attributes. An automated problem generator has been created to evolve the simulated datasets whilst the classification agent, in this case a learning classifier system, attempts to learn the evolving problem. The idea here is to tune the datasets autonomously such that the problem characteristics may be determined efficiently to empirically test the learning bounds of the classification agent by lowering human involvement. In this way, the effect of the problem's characteristics, which alter the classification agent's performance, becomes human readable. Tabu Search has been applied in the problem generator to discover the best combination of domain features in order to adjust the problem's complexity. Experiments confirm that the problem generator was able to tune the problem's complexity either to make the problem 'harder' or 'easier' so that it can either 'increase' or 'decrease' the classification agent's performance.
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Index Terms
- Adaptive artificial datasets through learning classifier systems for classification tasks
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